Signal processing apparatus and method

ABSTRACT

A signal processor which acquires a first signal, including a first desired signal portion and a first undesired signal portion, and a second signal, including a second desired signal portion and a second undesired signal portion, wherein the first and second desired signal portions are correlated. The signals may be acquired by propagating energy through a medium and measuring an attenuated signal after transmission or reflection. Alternatively, the signals may be acquired by measuring energy generated by the medium. A processor of the present invention generates a noise reference signal which is a combination of only the undesired signal portions and is correlated to both the first and second undesired signal portions. The noise reference signal is then used to remove the undesired portion of each of the first and second measured signals via an adaptive noise canceler, preferably of the joint process estimator type. The processor of the present invention may be employed in conjunction with an adaptive noise canceler in physiological monitors wherein the known properties of energy attenuation through a medium are used to determine physiological characteristics of the medium. Many physiological conditions, such as the pulse of a patient or the concentration of a constituent in a medium, can be determined from the desired portion of the signal after undesired signal portions, such as those caused by erratic motion, are removed.

CONTINUING APPLICATION DATA

This is a division of U.S. patent application Ser. No. 08/249,690, filedMay 26, 1994 (now U.S. Pat. No. 5,482,036), which is a continuation ofU.S. patent application Ser. No. 07/666,060, filed Mar. 7, 1991.

FIELD OF THE INVENTION

The present invention relates to the field of signal processing. Morespecifically, the present invention relates to the processing ofmeasured signals to remove undesired portions when little is known aboutthe undesired signal portion.

BACKGROUND OF THE INVENTION

Signal processors are typically employed to remove undesired portionsfrom a composite measured signal including a desired signal portion andan undesired signal portion. If the undesired signal portion occupies adifferent frequency spectrum than the desired signal, then conventionalfiltering techniques such as low pass, band pass, and high passfiltering could be used to separate the desired portion from the totalsignal. Fixed single or multiple notch filters could also be employed ifthe undesired signal portions(s) exist at a fixed frequency(s).

However, it is often the case that an overlap in frequency spectrumbetween the desired and undesired signal portions does exist and thestatistical properties of both signal portions change with time. In suchcases, conventional filtering techniques are totally ineffective inextracting the desired signal. If, however, a description of theundesired portion can be made available, adaptive noise canceling can beemployed to remove the undesired portion of the signal leaving thedesired portion available for measurement. Adaptive noise cancelersdynamically change their transfer function to adapt to and remove theundesired signal portions of a composite signal. Adaptive noisecancelers require a noise reference signal which is correlated to theundesired signal portion. The noise reference signal is not necessarilya representation of the undesired signal portion, but has a frequencyspectrum which is similar to that of the undesired signal. In manycases, it requires considerable ingenuity to determine a noise referencesignal since nothing is a priori known about the undesired signalportion.

One area where composite measured signals comprise a desired signalportion and an undesired signal portion about which no information caneasily be determined is physiological monitoring. Physiologicalmonitoring apparatuses generally measure signals derived from aphysiological system, such as the human body. Measurements which aretypically taken with physiological monitoring systems include electroncardiographs, blood pressure, blood gas saturation (such as oxygensaturation), capnographs, heart rate, respiration rate, and depth ofanesthesia, for example. Other types of measurements include those whichmeasure the pressure and quantity of a substance within the body such asbreathalizer testing, drug testing, cholesterol testing, glucosetesting, arterial carbon dioxide testing, protein testing, and carbonmonoxide testing, for example. The source of the undesired signalportion in these measurements is often due to motion of the patient,both external and internal (muscle movement, for example), during themeasurement process.

Knowledge of physiological systems, such as the amount of oxygen in apatient's blood, can be critical, for example during surgery. Data canbe determined by a lengthy invasive procedure of extracting and testingmatter, such as blood, from a patient, or by more expedient,non-invasive measures. Many types of non-invasive measurements can bemade by using the known properties of energy attenuation as a selectedform of energy passes through a medium.

Energy is caused to be incident on a medium either derived from orcontained within a patient and the amplitude of transmitted or reflectedenergy is then measured. The amount of attenuation of the incidentenergy caused by the medium is strongly dependent on the thickness andcomposition of the medium through which the energy must pass as well asthe specific form of energy selected. Information about a physiologicalsystem can be derived from data taken from the attenuated signal of theincident energy transmitted through the medium if the noise can beremoved. However, non-invasive measurements often do not afford theopportunity to selectively observe the interference causing theundesired signal portion, making it difficult to remove.

These undesired signal portions often originate from both AC and DCsources. The first undesired portion is an easily removed DC componentcaused by transmission of the energy through differing media which areof relatively constant thickness within the body, such as bone, tissue,skin, blood, etc. Second, is an erratic AC component caused whendiffering media being measured are perturbed and thus, change inthickness while the measurement is being made. Since most materials inand derived from the body are easily compressed, the thickness of suchmatter changes if the patient moves during a non-invasive physiologicalmeasurement. Patient movement can cause the properties of energyattenuation to vary erratically. Traditional signal filtering techniquesare frequently totally ineffective and grossly deficient in removingthese motion induced effects from a signal. The erratic or unpredictablenature of motion induced undesired signal components is the majorobstacle in removing them. Thus, presently available physiologicalmonitors generally become totally inoperative during time periods whenthe patient moves.

A blood gas monitor is one example of a physiological monitoring systemwhich is based upon the measurement of energy attenuated by biologicaltissues or substances. Blood gas monitors transmit light into the tissueand measure the attenuation of the light as a function of time. Theoutput signal of a blood gas monitor which is sensitive to the arterialblood flow contains a component which is a waveform representative ofthe patient's arterial pulse. This type of signal, which contains acomponent related to the patient's pulse, is called a plethysmographicwave, and is shown in FIG. 1 as curve Y. Plethysmographic waveforms areused in blood pressure or blood gas saturation measurements, forexample. As the heart beats the amount of blood in the arteriesincreases and decreases, causing increases and decreases in energyattenuation, illustrated by the cyclic wave Y in FIG. 1.

Typically, a digit such as a finger, an ear lobe, or other portion ofthe body where blood flows close to the skin, is employed as the mediumthrough which light energy is transmitted for blood gas attenuationmeasurements. The finger comprises skin, fat, bone, muscle, etc., shownschematically in FIG. 2, each of which attenuates energy incident on thefinger in a generally predictable and constant manner. However, whenfleshy portions of the finger are compressed erratically, for example bymotion of the finger, energy attenuation becomes erratic.

An example of a more realistic measured waveform S is shown in FIG. 3,illustrating the effect of motion. The desired portion of the signal Yis the waveform representative of the pulse, corresponding to thesawtooth-like pattern wave in FIG. 1. The large, motion-inducedexcursions in signal amplitude hide the desired signal Y. It is easy tosee how even small variations in amplitude make it difficult todistinguish the desired signal Y in the presence of a noise component n.

A specific example of a blood gas monitoring apparatus is a pulseoximeter which measures the saturation of oxygen in the blood. Thepumping of the heart forces freshly oxygenated blood into the arteriescausing greater energy attenuation. The saturation of oxygenated bloodmay be determined from the depth of the valleys relative to the peaks oftwo plethysmographic waveforms measured at separate wavelengths.However, motion induced undesired signal portions, or motion artifacts,must be removed from the measured signal for the oximeter to continuethe measurement during periods when the patient moves.

SUMMARY OF THE INVENTION

The present invention is a signal processor which acquires a firstsignal and a second signal that is correlated to the first signal. Thefirst signal comprises a first desired signal portion and a firstundesired signal portion. The second signal comprises a second desiredsignal portion and a second undesired signal portion. The signals may beacquired by propagating energy through a medium and measuring anattenuated signal after transmission or reflection. Alternatively, thesignal may be acquired by measuring energy generated by the medium.

The first and second measured signals are processed to generate a noisereference signal which does not contain the desired signal portions fromeither of the first or second measured signals. The remaining undesiredsignal portions from the first and second measured signals are combinedto form a noise reference signal. This noise reference signal iscorrelated to the undesired signal portion of each of the first andsecond measured signals.

The noise reference signal is then used to remove the undesired portionof each of the first and second measured signals via an adaptive noisecanceler. An adaptive noise canceler can be described by analogy to adynamic multiple notch filter which dynamically changes its transferfunction in response to the noise reference signal and the measuredsignals to remove frequencies from the measured signals that are alsopresent in the noise reference signal. Thus, a typical adaptive noisecanceler receives the signal from which it is desired to remove noiseand a noise reference signal. The output of the adaptive noise canceleris a good approximation to the desired signal with the noise removed.

Physiological monitors can often advantageously employ signal processorsof the present invention. Often in physiological measurements a firstsignal comprising a first desired portion and a first undesired portionand a second signal comprising a second desired portion and a secondundesired portion are acquired. The signals may be acquired bypropagating energy through a patient's body (or a material which isderived from the body, such as breath, blood, or tissue, for example)and measuring an attenuated signal after transmission or reflection.Alternatively, the signal may be acquired by measuring energy generatedby a patient's body, such as in electrocardiography. The signals areprocessed via the signal processor of the present invention to acquire anoise reference signal which is input to an adaptive noise canceler.

One physiological monitoring apparatus which can advantageouslyincorporate the features of the present invention is a monitoring systemwhich determines a signal which is representative of the arterial pulse,called a plethysmographic wave. This signal can be used in bloodpressure calculations, blood gas saturation measurements, etc. Aspecific example of such a use is in pulse oximetry which determines thesaturation of oxygen in the blood. In this configuration, the desiredportion of the signal is the arterial blood contribution to attenuationof energy as it passes through a portion of the body where blood flowsclose to the skin. The pumping of the heart causes blood flow toincrease and decrease in the arteries in a periodic fashion, causingperiodic attenuation wherein the periodic waveform is theplethysmographic waveform representative of the pulse.

A physiological monitor particularly adapted to pulse oximetry oxygensaturation measurement comprises two light emitting diodes (LED's) whichemit light at different wavelengths to produce first and second signals.A detector registers the attenuation of the two different energy signalsafter each passes through an absorptive media, for example a digit suchas a finger, or an earlobe. The attenuated signals generally compriseboth desired and undesired signal portions. A static filtering system,such as a band pass filter, removes a portion of the undesired signalwhich is static, or constant, or outside of a known bandwidth ofinterest, leaving an erratic or random undesired signal portion, oftencaused by motion and often difficult to remove, along with the desiredsignal portion.

Next, a processor of the present invention removes the desired signalportions from the measured signals yielding a noise reference signalwhich is a combination of the remaining undesired signal portions. Thenoise reference signal is correlated to both of the undesired signalportions. The noise reference signal and at least one of the measuredsignals are input to an adaptive noise canceler which removes the randomor erratic portion of the undesired signal. This yields a goodapproximation to the desired plethysmographic signal as measured at oneof the measured signal wavelengths. As is known in the art, quantitativemeasurements of the amount of oxygenated blood in the body can bedetermined from the plethysmographic signal in a variety of ways.

One aspect of the present invention is a signal processor comprising adetector for receiving a first signal which travels along a firstpropagation path and a second signal which travels along a secondpropagation path wherein a portion of the first and second propagationpaths are located in a propagation medium. The first signal has a firstdesired signal portion and a first undesired signal portion and thesecond signal has a second desired signal portion and a second undesiredsignal portion. The first and second undesired signal portions are aresult of a perturbation of the propagation medium. This aspect of theinvention additionally comprises a reference processor having an inputfor receiving the first and second signals. The processor is adapted tocombine the first and second signals to generate a reference signalhaving a primary component which is a function of the first and saidsecond undesired signal portions.

The above described aspect of the present invention may further comprisean adaptive signal processor for receiving the reference signal and thefirst signal and for deriving therefrom an output signal having aprimary component which is a function of the first desired signalportion of the first signal. Alternatively, the above described aspectof the present invention my further comprise an adaptive signalprocessor for receiving the reference signal and the second signal andfor deriving therefrom an output signal having a primary component whichis a function of the second desired signal portion of the second signal.The adaptive signal processor may comprise an adaptive noise canceler.The adaptive noise canceler may be comprise a joint process estimatorhaving a least-squares-lattice predictor and a regression filter.

The detector in the aspect of the signal processor of the presentinvention described above may further comprise a sensor for sensing aphysiological function. The sensor may comprise a light sensitivedevice. Additionally, the present invention may further comprising apulse oximeter for measuring oxygen saturation in a living organism.

Another aspect of the present invention is a physiological monitoringapparatus comprising a detector for receiving a first physiologicalmeasurement signal which travels along a first propagation path and asecond physiological measurement signal which travels along a secondpropagation path. A portion of the first and second propagation paths islocated in a propagation medium. The first signal has a first desiredsignal portion and a first undesired signal portion and the secondsignal has a second desired signal portion and a second undesired signalportion. The physiological monitoring apparatus further comprises areference processor having an input for receiving the first and secondsignals. The processor is adapted to combine the first and secondsignals to generate a reference signal having a primary component whichis a function of the first and the second undesired signal portions.

The physiological monitoring apparatus may further comprise an adaptivesignal processor for receiving the reference signal and the first signaland for deriving therefrom an output signal having a primary componentwhich is a function of the first desired signal portion of the firstsignal. Alternatively, the physiological monitoring apparatus mayfurther comprise an adaptive signal processor for receiving thereference signal and the second signal and for deriving therefrom anoutput signal having a primary component which is a function of thesecond desired signal portion of the second signal. The physiologicalmonitoring apparatus may further comprise a pulse oximeter.

A further aspect of the present invention is an apparatus for measuringa blood constituent comprising an energy source for directing aplurality of predetermined wavelengths of electromagnetic energy upon aspecimen and a detector for receiving the plurality of predeterminedwavelengths of electromagnetic energy from the specimen. The detectorproduces electrical signals corresponding to the predeterminedwavelengths in response to the electromagnetic energy. At least two ofthe electrical signals each has a desired signal portion and anundesired signal portion. Additionally, the apparatus comprises areference processor having an input for receiving the electricalsignals. The processor is configured to combine said electrical signalsto generate a reference signal having a primary component which isderived from the undesired signal portions.

This aspect of the present invention may further comprise an adaptivesignal processor for receiving the reference signal and one of the twoelectrical signals and for deriving therefrom an output signal having aprimary component which is a function of the desired signal portion ofthe electrical signal. This may be accomplished by use of an adaptivenoise canceler in the adaptive signal processor which may employ a jointprocess estimator having a least-squares-lattice predictor and aregression filter.

Yet another aspect of the present invention is a blood gas monitor fornon-invasively measuring a blood constituent in a body comprising alight source for directing at least two predetermined wavelengths oflight upon a body and a detector for receiving the light from the body.The detector, in response to the light from the body, produces at leasttwo electrical signals corresponding to the at least two predeterminedwavelengths of light. The at least two electrical signals each has adesired signal portion and an undesired signal portion. The bloodoximeter further comprises a reference processor having an input forreceiving the at least two electrical signals. The processor is adaptedto combine the at least two electrical signals to generate a referencesignal with a primary component which is derived from the undesiredsignal portions. The blood oximeter may further comprise an adaptivesignal processor for receiving the reference signal and the twoelectrical signals and for deriving therefrom at least two outputsignals which are substantially equal, respectively, to the desiredsignal portions of the electrical signals.

The present invention also includes a method of determining a noisereference signal from a first signal comprising a first desired signalportion and a first noise portion and a second signal comprising asecond desired signal portion and a second noise portion. The methodcomprises the steps of selecting a signal coefficient which isproportional to a ratio of predetermined attributes of the first desiredsignal portion and predetermined attributes of the second desired signalportion. The first signal and the second signal coefficient are inputinto a signal multiplier wherein the first signal is multiplied by thesignal coefficient thereby generating a first intermediate signal. Thesecond signal and the first intermediate signal are input into a signalsubtractor wherein the first intermediate signal is subtracted from thesecond signal. This generates a noise reference signal having a primarycomponent which is derived from the first and second noise signalportions. The first and second signals in this method may be derivedfrom light energy transmitted through an absorbing medium.

The present invention further embodies a physiological monitoringapparatus comprising means for acquiring a first signal comprising afirst desired signal portion and a first undesired signal portion and asecond signal comprising a second desired signal portion and a secondundesired signal portion. The physiological monitoring apparatus of thepresent invention also comprises means for determining from the firstand second signals a noise reference signal. Additionally, themonitoring apparatus comprises an adaptive noise canceler having a noisereference input for receiving the noise reference signal and a signalinput for receiving the first signal wherein the adaptive noisecanceler, in real or near real time, generates an output signal whichapproximates the first desired signal portion. The adaptive noisecanceler may further comprise a joint process estimator.

A further aspect of the present invention is an apparatus for processingan amplitude modulated signal having a signal amplitude complicatingfeature, the apparatus comprising an energy source for directingelectromagnetic energy upon a specimen. Additionally, the apparatuscomprises a detector for acquiring a first amplitude modulated signaland a second amplitude modulated signal. Each of the first and secondsignals has a component containing information about the attenuation ofelectromagnetic energy by the specimen and a signal amplitudecomplicating feature. The apparatus includes a reference processor forreceiving the first and second amplitude modulated signals and derivingtherefrom a noise reference signal which is correlated with the signalamplitude complicating feature. Further, the apparatus incorporates anadaptive noise canceler having a signal input for receiving the firstamplitude modulated signal, a noise reference input for receiving thenoise reference signal, wherein the adaptive noise canceler produces anoutput signal having a primary component which is derived from thecomponent containing information about the attenuation ofelectromagnetic energy by the specimen.

Still another aspect of the present invention is an apparatus forextracting a plethysmographic waveform from an amplitude modulatedsignal having a signal amplitude complicating feature, the apparatuscomprising a light source for transmitting light into an organism and adetector for monitoring light from the organism. The detector produces afirst light attenuation signal and a second light attenuation signal,wherein each of the first and second light attenuation signals has acomponent which is representative of a plethysmographic waveform and acomponent which is representative of the signal amplitude complicatingfeature. The apparatus also includes a reference processor for receivingthe first and second light attenuation signals and deriving therefrom anoise reference signal. The noise reference signal and the signalamplitude complicating feature each has a frequency spectrum. Thefrequency spectrum of the noise reference signal is correlated with thefrequency spectrum of the signal amplitude complicating feature.Additionally incorporated into this embodiment of the present inventionis an adaptive noise canceler having a signal input for receiving thefirst attenuation signal and a noise reference input for receiving thenoise reference signal. The adaptive noise canceler produces an outputsignal having a primary component which is derived from the componentwhich is representative of a plethysmographic waveform.

The present invention also comprises a method of removing a motionartifact signal from a signal derived from a physiological measurementwherein a first signal having a physiological measurement component anda motion artifact component and a second signal having a physiologicalmeasurement component and a motion artifact component are acquired. Fromthe first and second signals a motion artifact noise reference signalwhich is a primary function of the first and second signals motionartifact components is derived. This method of removing a motionartifact signal from a signal derived from a physiological measurementmay also comprise the step of inputting the motion artifact noisereference signal into an adaptive noise canceler to produce an outputsignal which is a primary function of the first signal physiologicalmeasurement component.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an ideal plethysmographic waveform.

FIG. 2 schematically illustrates the cross-sectional structure of atypical finger.

FIG. 3 illustrates a plethysmographic waveform which includes amotion-induced undesired erratic signal portion.

FIG. 4 illustrates a schematic diagram of a physiological monitorincorporating a processor of the present invention,and an adaptive noisecanceler.

FIG. 4a illustrates the transfer function of a multiple notch filter.

FIG. 5 illustrates an example of an adaptive noise canceler which couldbe employed in a physiological monitor which also incorporates theprocessor of the present invention.

FIG. 6a illustrates a schematic absorbing material comprising Nconstituents within an absorbing material.

FIG. 6b illustrates another schematic absorbing material comprising Nconstituents within an absorbing material.

FIG. 7 is a schematic model of a joint process estimator comprising aleast-squares lattice predictor and a regression filter.

FIG. 8 a flowchart representing a subroutine capable of implementing ajoint process estimator as modeled in FIG. 7.

FIG. 9 is a schematic model of a joint process estimator with aleast-squares lattice predictor and two regression filter.

FIG. 10 is an example of a physiological monitor incorporating aprocessor of the present invention and an adaptive noise canceler withina microprocessor. This physiological monitor is specifically designed tomeasure a plethysmographic waveform and perform pulse oximetrymeasurements.

FIG. 11 is a graph of oxygenated and deoxygenated absorptioncoefficients vs. wavelength.

FIG. 12 is a graph of the ratio of the absorption coefficients ofdeoxygenated hemoglobin divided by oxygenated hemoglobin vs. wavelength.

FIG. 13 is an expanded view of a portion of FIG. 11 marked by a circlelabelled 13.

FIG. 14 illustrates a signal measured at a first red wavelengthλa=λred1=650 nm for use in a processor of the present inventionemploying the ratiometric method for determining the noise referencesignal n'(t) and for use in a joint process estimator. The measuredsignal comprises a desired portion Y₈₀ a (t) and an undesired portionn.sub.λa (t).

FIG. 15 illustrates a signal measured at a second red wavelengthλb=λred2=685 nm for use in a processor of the present inventionemploying the ratiometric method for determining the noise referencesignal n'(t). The measured signal comprises a desired portion Y.sub.λb(t) and an undesired portion n.sub.λb (t).

FIG. 16 illustrates a signal measured at an infrared wavelengthλc=λIR=940 nm for use in a joint process estimator. The measured signalcomprises a desired portion Y.sub.λc (t) and an undesired portionn.sub.λc (t).

FIG. 17 illustrates the noise reference signal n'(t) determined aprocessor of the present invention using the ratiometric method.

FIG. 18 illustrates a good approximation Y'.sub.λa (t) to the desiredportion Y.sub.λa (t) of the signal S.sub.λa (t) measured at λa=λred1=650nm estimated with a noise reference signal n'(t) determined by theratiometric method.

FIG. 19 illustrates a good approximation Y'.sub.λc (t) to the desiredportion Y.sub.λc (t) of the signal S.sub.λc (t) measured at λc=λIR=940nm estimated with a noise reference signal n'(t) determined by theratiometric method.

FIG. 20 illustrates a signal measured at a red wavelength λa=λred=660 nmfor use in a processor of the present invention employing the constantsaturation method for determining the noise reference signal n'(t) andfor use in a joint process estimator. The measured signal comprises adesired portion Y.sub.λa (t) and an undesired portion n.sub.λa (t).

FIG. 21 illustrates a signal measured at an infrared wavelengthλb=λIR=940 nm for use in a processor of the present invention employingthe constant saturation method for determining the noise referencesignal n'(t) and for use in a joint process estimator. The measuredsignal comprises a desired portion Y.sub.λb (t) and an undesired portionn.sub.λb (t).

FIG. 22 illustrates the noise reference signal n'(t) determined by aprocessor of the present invention using the constant saturation method.

FIG. 23 illustrates a good approximation Y'.sub.λa (t) to the desiredportion Y.sub.λa (t) of the signal S.sub.λa (t) measured at λa=λred=660nm estimated with a noise reference signal n'(t) determined by theconstant saturation method.

FIG. 24 illustrates a good approximation Y'.sub.λb (t) to the desiredportion Y.sub.λb (t) of the signal S.sub.λb (t) measured at λb=λIR=940nm estimated with a noise reference signal n'(t) determined by theconstant saturation method.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is a processor which determines a noise referencesignal n'(t) for use in an adaptive noise canceler. An adaptive noisecanceler estimates a good approximation Y'(t) to a desired signal Y(t)from a composite signal S(t)=Y(t)+n(t) which, in addition to the desiredportion Y(t) comprises an undesired portion n(t). The undesired portionn(t) may contain one or more of a constant portion, a predictableportion, an erratic portion, a random portion, etc. The approximation tothe desired signal Y'(t) is derived by removing as many of the undesiredportions n(t) from the composite signal S(t) as possible. The constantportion and predictable portion are easily removed with traditionalfiltering techniques, such as simple subtraction, low pass, band pass,and high pass filtering. The erratic portion is more difficult to removedue to its unpredictable nature. If something is known about the erraticsignal, even statistically, it could be removed from the measured signalvia traditional filtering techniques. However, it is often the case thatno information is known about the erratic portion of the noise. In thiscase, traditional filtering techniques are usually insufficient. Oftenno information about the erratic portion of the measured signal isknown. Thus, an adaptive noise canceler is utilized in the presentinvention to remove the erratic portion.

Generally, an adaptive noise canceler has two signal inputs and oneoutput. One of the inputs is the noise reference signal n'(t) which iscorrelated to the erratic undesired signal portions n(t) present in thecomposite signal S(t). The other input is for the composite signal S(t).Ideally, the output of the adaptive noise canceler Y'(t) corresponds tothe desired signal portion Y(t) only. Often, the most difficult task inthe application of adaptive noise cancelers is determining the noisereference signal n'(t) which is correlated to the erratic undesiredportion n(t) of the measured signal S(t) since, as discussed above,unpredictable signal portions are usually quite difficult to isolatefrom the measured signal S(t). In the signal processor of the presentinvention, a noise reference signal n'(t) is determined from twocomposite signals measured simultaneously, or nearly simultaneously, attwo different wavelengths, λa and λb. The signal processor of thepresent invention can be advantageously used in a monitoring device,such a monitor being well suited for physiological monitoring.

A block diagram of a generic monitor incorporating a signal processor,or reference processor, according to the present invention and anadaptive noise canceler is shown in FIG. 4. Two measured signals,S.sub.λa (t) and S.sub.λb (t), are acquired by a detector 20. Oneskilled in the art will realize that for some physiologicalmeasurements, more than one detector may be advantageous. Each signal isconditioned by a signal conditioner 22a and 22b. Conditioning includes,but is not limited to, such procedures as filtering the signals toremove constant portions and amplifying the signals for ease ofmanipulation. The signals are then converted to digital data by ananalog-to-digital converter 24a and 24b. The first measured signalS.sub.λa (t) comprises a first desired signal portion, labelled hereinY.sub.λa (t), and a first undesired signal portion, labelled hereinn.sub.λa (t). The second measured signal S.sub.λb (t) is at leastpartially correlated to the first measured signal S.sub.λa (t) andcomprises a second desired signal portion, labelled herein Y.sub.λb (t),and a second undesired signal portion, labelled herein n.sub.λb (t).Typically the first and second undesired signal portions, n.sub.λa (t)and n.sub.λb (t), are uncorrelated and/or erratic with respect to thedesired signal portions Y.sub.λa (t) and Y.sub.λb (t). The undesiredsignal portions n.sub.λa (t) and n.sub.λb (t) are often caused by motionof a patient. The signals S.sub.λa (t) and S.sub.λb (t) are input to areference processor 26. The reference processor 26 multiplies the secondmeasured signal S.sub.λb (t) by a factor ω and then subtracts the secondmeasured signal S.sub.λb (t) from the first measured signal S.sub.λa(t). The factor w is determined to cause the desired signal portionsY.sub.λa (t) and Y.sub.λb (t) to cancel when the two signals S.sub.λa(t) and S.sub.λb (t) are subtracted. Thus, the output of the referenceprocessor 26 is a noise reference signal n'(t)=n.sub.λa (t)-ωn.sub.λb(t) which is correlated to both of the erratic undesired signal portionsn.sub.λa (t) and n.sub.λb (t). The noise reference signal n'(t) isinput, along with one of the measured signals S.sub.λa (t), to anadaptive noise canceler 27 which uses the noise reference signal n'(t)to remove the undesired signal portion n.sub.λa (t) or n.sub.λb (t) fromthe measured signal S.sub.λa (t). It will be understood that S.sub.λb(t) could have been input to the adaptive noise canceler 27 along withthe noise reference signal n'(t) instead of S.sub.λa (t). The output ofthe adaptive noise canceler 27 is a good approximation Y'.sub.λa (t) tothe desired signal Y.sub.λa (t). The approximation Y'.sub.λa (t) isdisplayed on the display 28.

An adaptive noise canceler 30, an example of which is shown in blockdiagram in FIG. 5, is employed to remove the erratic, undesired signalportions n.sub.λa (t) and n.sub.λb (t) from the measured signalsS.sub.λa (t) and S.sub.λb (t). The adaptive noise canceler 30 in FIG. 5has as one input a sample of the noise reference signal n'(t) which iscorrelated to the undesired signal portions n.sub.λa (t) and n.sub.λb(t). The noise reference signal n'(t) is determined from the twomeasured signals S.sub.λa (t) and S.sub.λb (t) by the processor 26 ofthe present invention as described herein. A second input to theadaptive noise canceler is a sample of either the first or secondmeasured signal S.sub.λa (t)=Y.sub.λa (t)+n.sub.λa (t) or S.sub.λb(t)=Y.sub.λb (t)+n.sub.λb (t).

The adaptive noise canceler 30 functions to remove frequencies common toboth the noise reference signal n'(t) and the measured signal S.sub.λa(t) or S.sub.λb (t). Since the noise reference signal n'(t) iscorrelated to the erratic undesired signal portions n.sub.λa (t) andn.sub.λb (t), the noise reference signal n'(t) is also erratic. Theadaptive noise canceler acts in a manner which may be analogized to adynamic multiple notch filter based on the spectral distribution of thenoise reference signal n'(t).

Referring to FIG. 4a, the transfer function of a multiple notch filteris shown. The notches, or dips in the amplitude of the transferfunction, indicate frequencies which are attenuated or removed when acomposite measured signal passes through the notch filter. The output ofthe notch filter is the composite signal having frequencies at which anotch was present removed. In the analogy to an adaptive noise canceler,the frequencies at which notches are present change continuously basedupon the inputs to the adaptive noise canceler.

The adaptive noise canceler 30 shown in FIG. 5 produces an outputsignal, labelled herein Y'.sub.λa (t) or Y'.sub.λb (t), which is fedback to an internal processor 32 within the adaptive noise canceler 30.The internal processor 32 automatically adjusts its own transferfunction according to a predetermined algorithm such that the output ofthe internal processor 32, labelled b(t), closely resembles theundesired signal portion n.sub.λa (t) or n.sub.λb (t). The output b(t)of the internal processor 32 is subtracted from the measured signal,S.sub.λa (t) or S.sub.λb (t), yielding a signal Y'.sub.λa (t)≈S.sub.λa(t)+n.sub.λa (t)-b.sub.λa (t) or Y'.sub.λb (t)≈S.sub.λb (t)+n.sub.λb(t)-b.sub.λb (t). The internal processor optimizes Y'.sub.λa (t) orY'.sub.λb (t) such that Y'.sub.λa (t) or Y'.sub.λb (t) is approximatelyequal to the desired signal Y.sub.λa (t) or Y.sub.λb (t), respectively.

One algorithm which may be used for the adjustment of the transferfunction of the internal processor 32 is a least-squares algorithm, asdescribed in Chapter 6 and Chapter 12 of the book Adaptive SignalProcessing by Bernard Widrow and Samuel Stearns, published by PrenticeHall, copyright 1985. This entire book, including Chapters 6 and 12, ishereby incorporated herein by reference.

Adaptive processors id have been successfully applied to a number ofproblems including antenna sidelobe canceling, pattern recognition, theelimination of periodic interference in general, and the elimination ofechoes on long distance telephone transmission lines. However,considerable ingenuity is often required to find a suitable noisereference signal n'(t) for a given application since the random orerratic portions n.sub.λa (t) or n.sub.λb (t) cannot easily be separatedfrom the measured signal S.sub.λa (t) or S.sub.λb (t). If the actualundesired signal portion n.sub.λa (t) or n.sub.λb (t) were a prioriavailable, techniques such as adaptive noise canceling would not benecessary. The unique determination of a suitable noise reference signaln'(t) from measurements taken by a monitor incorporating a referenceprocessor of the present invention is one aspect of the presentinvention.

GENERALIZED DETERMINATION OF NOISE REFERENCE SIGNAL

An explanation which describes how the noise reference signal n'(t) maybe determined as follows. A first signal is measured at, for example, awavelength λa, by a detector yielding a signal S.sub.λa (t):

    S.sub.λa (t)=Y.sub.λa (t)+n.sub.λa (t);(1)

where Y.sub.λa (t) is the desired signal and n.sub.λa (t) is the noisecomponent.

A similar measurement is taken simultaneously, or nearly simultaneously,at a different wavelength, λb, yielding:

    S.sub.λb (t)=Y.sub.λb (t)+n.sub.λb.   (2)

Note that as long as the measurements, S.sub.λa (t) and S.sub.λb (t),are taken substantially simultaneously, the undesired signal components,n.sub.λa (t) and n.sub.λb (t), will be correlated because any random orerratic functions will affect each measurement in nearly the samefashion.

To obtain the noise reference signal n'(t), the measured signalsS.sub.λa (t) and S.sub.λb (t) are transformed to eliminate the desiredsignal components. One way of doing this is to find a proportionalityconstant, ω₁, between the desired signals Y.sub.λa (t) and Y.sub.λb (t)such that:

    Y.sub.λa (t)=ω.sub.1 Y.sub.λb (t).     (3)

This proportionality relationship can be satisfied in many measurements,including but not limited to absorption measurements and physiologicalmeasurements. Additionally, in most measurements, the proportionalityconstant ω₁ can be determined such that:

    n.sub.λa (t)≠ω.sub.1 n.sub.λb (t). (4)

Multiplying equation (2) by ω.sub.ω₁ and then subtracting equation (2)from equation (1) results in a single equation wherein the desiredsignal terms Y.sub.λa (t) and S.sub.λb (t) cancel, leaving:

    n'(t)=S.sub.λa (t)-ω.sub.1 S.sub.λb (t)=n.sub.λ (t)-ω.sub.1 n.sub.λb (t);                    (5)

a non-zero signal which is correlated to each undesired signal portionn.sub.λa (t) and n.sub.λb (t) and can be used as the noise referencesignal n'(t) in an adaptive noise canceler.

EXAMPLE OF DETERMINATION OF NOISE REFERENCE SIGNAL IN AN ABSORPTIVESYSTEM

Adaptive noise canceling is particularly useful in a large number ofmeasurements generally described as absorption measurements. An exampleof an absorption type monitor which can advantageously employ adaptivenoise canceling based upon a noise reference signal n'(t) determined bya processor of the present invention is one which determines theconcentration of an energy absorbing constituent within an absorbingmaterial when the material is subject to perturbation. Suchperturbations can be caused by forces about which information isdesired, or alternatively, by random or erratic forces such as amechanical force on the material. Random or erratic interference, suchas motion, generates undesired noise components in the measured signal.These undesired components can be removed by the adaptive noise cancelerif a suitable noise reference signal n'(t) is known.

A schematic N constituent absorbing material comprising a container 42having N different absorbing constituents, labelled A₁, A₂, A₃, . . .A_(N), is shown schematically in FIG. 6a. The constituents A₁ throughA_(N) in FIG. 6a are arranged in a generally orderly, layered fashionwithin the container 42. An example of a particular type of absorptivesystem is one in which light energy passes through the container 42 andis absorbed according to the generalized Beer-Lambert Law of lightabsorption. For light of wavelength λa, this attenuation may beapproximated by: ##EQU1## Initially transforming the signal by takingthe natural log of both sides and manipulating terms, the signal istransformed such that the signal components are combined by additionrather than multiplication, i.e.: ##EQU2## where I₀ is the incidentlight energy intensity; I is the transmitted light energy intensity;ε_(i),λa is the absorption coefficient of the i^(th) constituent at thewavelength λa; x_(i) (t) is the optical path length of i^(th) layer,i.e., the thickness of material of the i^(th) layer through whichoptical energy passes; and c_(i) (t) is the concentration of the i^(th)constituent in the volume associated with the thickness x_(i) (t). Theabsorption coefficients ε₁ through ε_(N) are known values which areconstant at each wavelength. Most concentrations c₁ (t) through c_(N)(t) are typically unknown, as are most of the optical path lengths x_(i)(t) of each layer. The total optical path length is the sum of each ofthe individual optical path lengths x_(i) (t) of each layer.

When the material is not subject to any forces which cause perturbationin the thicknesses of the layers, the optical path length of each layer,x_(i) (t), is generally constant. This results in generally constantattenuation of the optical energy and thus, a generally constant offsetin the measured signal. Typically, this portion of the signal is oflittle interest since knowledge about a force which perturbs thematerial is usually desired. Any signal portion outside of a knownbandwidth of interest, including the constant undesired signal portionresulting from the generally constant absorption of the constituentswhen not subject to perturbation, should be removed. This is easilyaccomplished by traditional band pass filtering techniques. However,when the material is subject to forces, each layer of constituents maybe affected by the perturbation differently than each other layer. Someperturbations of the optical path lengths of each layer x_(i) (t) mayresult in excursions in the measured signal which represent desiredinformation. Other perturbations of the optical path length of eachlayer x_(i) (t) cause undesired excursions which mask desiredinformation in the measured signal. Undesired signal componentsassociated with undesired excursions must also be removed to obtaindesired information from the measured signal.

The adaptive noise canceler removes from the composite signal, measuredafter being transmitted through or reflected from the absorbingmaterial, the undesired signal components caused by forces which perturbthe material differently from the forces which perturbed the material tocause the desired signal component. For the purposes of illustration, itwill be assumed that the portion of the measured signal which is deemedthe desired signal Y.sub.λa (t) is the attenuation term ε₅ c₅ x₅ (t)associated with a constituent of interest, namely A₅, and that the layerof constituent A₅ is affected by perturbations differently than each ofthe layers of other constituents A₁ through A₄ and A₆ through A_(N). Anexample of such a situation is when layer A₅ is subject to forces aboutwhich information is desired and, additionally, the entire material issubject to forces which affect each of the layers. In this case, sincethe total force affecting the layer of constituents A₅ is different thanthe total forces affecting each of the other layers and information isdesired about the forces and resultant perturbation of the layer ofconstituents A₅, attenuation terms due to constituents A₁ through A₄ andA₆ through A_(N) make up the undesired signal n.sub.λa (t). Even if theadditional forces which affect the entire material cause the sameperturbation in each layer, including the layer of A₅, the total forceson the layer of constituent A₅ cause it to have different totalperturbation than each of the other layers of constituents A₁ through A₄and A₆ through A_(N).

It is often the case that the total perturbation affecting the layersassociated with the undesired signal components is caused by random orerratic forces. This causes the thickness of layers to changeerratically and the optical path length of each layer, x_(i) (t), tochange erratically, thereby producing a random or erratic undesiredsignal component n.sub.λa (t). However, regardless of whether or not theundesired signal portion n.sub.λa (t) is erratic, the undesired signalcomponent n.sub.λa (t) can be removed via an adaptive noise cancelerhaving as one input a noise reference signal n'(t) determined by aprocessor of the present invention as long as the perturbation on layersother than the layer of constituent A₅ is different than theperturbation on the layer of constituent A₅. The adaptive noise canceleryields a good approximation to the desired signal Y'.sub.λa (t). Fromthis approximation, the concentration of the constituent of interest, c₅(t), can often be determined since in some physiological measurements,the thickness of the desired signal component, x₅ (t) in this example,is known or can be determined.

The adaptive noise canceler utilizes a sample of a noise referencesignal n'(t) determined from two substantially simultaneously measuredsignals S.sub.λa (t) and S.sub.λb (t). S.sub.λa (t) is determined asabove in equation (7). S.sub.λb (t) is determined similarly at adifferent wavelength λb. To find the noise reference signal n'(t),attenuated transmitted energy is measured at the two differentwavelengths λa and λb and transformed via logarithmic conversion. Thesignals S.sub.λa (t) and S.sub.λb (t) can then be written (logarithmconverted) as: ##EQU3##

A further transformation of the signals is the proportionalityrelationship defining ω₂, similarly to equation (3), which allowsdetermination of a noise reference signal n'(t), is:

    ε.sub.5,λa =ω.sub.2 ε.sub.5,λb ;(12)

where

    n.sub.λa ≠ω.sub.2 n.sub.λb.      (13)

It is often the case that both equations (12) and (13) can besimultaneously satisfied. Multiplying equation (11) by ω₂ andsubtracting the result from equation (9) yields a non-zero noisereference signal which is a linear sum of undesired signal components:##EQU4##

A sample of this noise reference signal n'(t), and a sample of eithermeasured signal S.sub.λa (t) or S.sub.λb (t), are input to an adaptivenoise canceler, one model of which is shown in FIG. 5 and a preferredmodel of which is discussed herein under the heading PREFERRED ADAPTIVENOISE CANCELER USING A JOINT PROCESS ESTIMATOR IMPLEMENTATION. Theadaptive noise canceler removes the undesired portion of the measuredsignal n.sub.λa (t) or n.sub.λb (t), yielding a good approximation tothe desired portion of signal Y'.sub.λa (t)≈ε₅,λa c₅ x₅ (t) or Y'.sub.λb(t)≈ε₅,λb c₅ x₅ (t). The concentration c₅ (t) may then be determinedfrom the approximation to the desired signal Y'.sub.λa (t) or Y'.sub.λb(t) according to:

    c.sub.5 (t)≈Y'.sub.λa (t)/ε.sub.5,λa x.sub.5 (t)≈Y'.sub.λb (t)/ε.sub.5,λb x.sub.5 (t).(17)

As discussed previously, the absorption coefficients are constant ateach wavelength λa and λb and the thickness of the desired signalcomponent, (x₅ (t) in this example, is often known or can be determinedas a function of time, thereby allowing calculation of the concentrationc₅ (t) of constituent A₅.

DETERMINATION OF CONCENTRATION OR SATURATION IN A VOLUME CONTAINING MORETHAN ONE CONSTITUENT

Referring to FIG. 6b, another material having N different constituentsarranged in layers is shown. In this material, two constituents A₅ andA₆ are found within one layer having thickness X₅,6 (t)=x₅ (t)+x₆ (t),located generally randomly within the layer. This is analogous tocombining the layers of constituents A₅ and A₆ in FIG. 6a. A combinationof layers, such as the combination of layers of constituents A₅ and A₆,is feasible when the two layers are under the same total forces whichresult in the same perturbation of the optical path lengths x₅ (t) andx₆ (t) of the layers.

Often it is desirable to find the concentration or the saturation, i.e.,a percent concentration, of one constituent within a given thicknesswhich contains more than one constituent and is subject to uniqueforces. A determination of the concentration or the saturation of aconstituent within a given volume may be made with any number ofconstituents in the volume subject to the same total forces andtherefore under the same perturbation. To determine the saturation ofone constituent in a volume comprising many constituents, as manymeasured signals as there are constituents which absorb incident lightenergy are necessary. It will be understood that constituents which donot absorb light energy are not consequential in the determination ofsaturation. To determine the concentration, as many signals as there areconstituents which absorb incident light energy are necessary as well asinformation about the sum of concentrations.

It is often the case that a thickness under unique motion contains onlytwo constituents. For example, it may be desirable to know theconcentration or saturation of A₅ within a given volume which containsA₅ and A₆. In this case, the desired signals Y.sub.λa (t) and Y.sub.λb(t) comprise terms related to both A₅ and A₆ so that a determination ofthe concentration or saturation of A₅ or A₆ in the volume may be made. Adetermination of saturation is discussed herein. It will be understoodthat the concentration of A₅ in volume containing both A₅ and A₆ couldalso be determined if it is known that A₅ +A₆ =1, i.e., that there areno constituents in the volume which do not absorb incident light energyat the particular measurement wavelengths chosen. The measured signalsS.sub.λa (t) and S.sub.λb (t) can be written (logarithm converted) as:

    S.sub.λa (t)=ε.sub.5,λa c.sub.5 x.sub.5,6 (t)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t)+n.sub.λa (t)(18)

    =Y.sub.λa (t)+n.sub.λa (t);                  (19)

    S.sub.λb (t)=ε.sub.5,λb c.sub.5 x.sub.5,6 (t)+ε.sub.6,λb c.sub.6 x.sub.5,6 (t)+n.sub.λb (t)(20)

    =Y.sub.λb (t)+n.sub.λb (t).                  (21)

Any signal portions outside of a known bandwidth of interest, includingthe constant undesired signal portion-resulting from the generallyconstant absorption of the constituents when not under perturbation,should be removed to determine an approximation to the desired signal.This is easily accomplished by traditional band pass filteringtechniques. As in the previous example, it is often the case that thetotal perturbation affecting the layers associated with the undesiredsignal components is caused by random or erratic forces, causing thethickness of each layer, or the optical path length of each layer, x_(i)(t), to change erratically, producing a random or erratic undesiredsignal component n.sub.λa (t). Regardless of whether or not theundesired signal portion n.sub.λa (t) is erratic, the undesired signalcomponent n.sub.λa (t) can be removed via an adaptive noise cancelerhaving as one input a noise reference signal n'(t) determined by aprocessor of the present invention as long as the perturbation in layersother than the layer of constituents A₅ and A₆ is different than theperturbation in the layer of constituents A₅ and A₆. The erraticundesired signal components n.sub.λa (t) and n.sub.λb (t) mayadvantageously be removed from equations (18) and (20), or alternativelyequations (19) and (21), by an adaptive noise canceler. The adaptivenoise canceler, again, requires a sample of a noise reference signaln'(t).

DETERMINATION OF NOISE REFERENCE SIGNAL FOR SATURATION MEASUREMENT

Two methods which may be used by a processor of the present invention todetermine the noise reference signal n'(t) are a ratiometric method anda constant saturation method. The preferred embodiment of aphysiological monitor incorporating a processor of the present inventionutilizes the ratiometric method wherein the two wavelengths λa and λb,at which the signals S.sub.λa (t) and S.sub.λb (t) are measured, arespecifically chosen such that a relationship between the absorptioncoefficients ε₅,λa, ε₅,λb, ε₆,λa and ε₆,λb exists, i.e.:

    ε.sub.5,λa /ε.sub.6,λa =ε.sub.5,λb /ε.sub.6,λb     (22)

The measured signals S.sub.λa (t) and S.sub.λb (t) can be factored andwritten as:

    S.sub.λa (t)=ε.sub.6,λa  (ε.sub.5,λa /ε.sub.6,λa)c.sub.5 x(t)+c.sub.6 x(t)!+n.sub.λa (t)(23)

    S.sub.λb (t)=ε.sub.6,λb  (ε.sub.5,λb /ε.sub.6,λb)c.sub.5 x(t)+c.sub.6 x(t)!+n.sub.λb (t).(24)

The wavelengths λa and λb, chosen to satisfy equation (22), cause theterms within the square brackets to be equal, thereby causing thedesired signal portions Y'.sub.λa (t) and Y'.sub.λb (t) to be linearlydependent. Then, a proportionality constant ω_(r3) which causes thedesired signal portions Y'.sub.λa (t) and Y'.sub.λb (t) to be equal andallows determination of a non-zero noise reference signal n'(t) is:

    ε.sub.6,λa =ω.sub.r3 ε.sub.6,λb ;(25)

where

    n.sub.λa ≠ω.sub.r3 n.sub.λb.     (26)

It is often the case that both equations (25) and (26) can besimultaneously satisfied. Additionally, since absorption coefficients ofeach constituent are constant with respect to wavelength, theproportionality constant ω_(r3) can be easily determined. Furthermore,absorption coefficients of other constituents A₁ through A₄ and A₇through A_(N) are generally unequal to the absorption coefficients of A₅and A₆. Thus, the undesired noise components n.sub.λa and n.sub.λb aregenerally not made linearly dependent by the relationships of equations(22) and (25).

Multiplying equation (24) by ω_(r3) and subtracting the resultingequation from equation (23), a non-zero noise reference signal isdetermined by:

    n'(t)=S.sub.λa (t)-ω.sub.r3 S.sub.λb (t)=n.sub.λa (t)-ω.sub.r3 n.sub.λb (t).(27)

An alternative method for determining the noise reference signal fromthe measured signals S.sub.λa (t) and S.sub.λb (t) using a processor ofthe present invention is the constant saturation approach. In thisapproach, it is assumed that the saturation of A₅ in the volumecontaining A₅ and A₆ remains relatively constant, i.e.:

    Saturation(A.sub.5 (t))=c.sub.5 (t)/ c.sub.5 (t)+c.sub.6 (t)!(28)

    ={1+ c.sub.6 (t)/c.sub.5 (t)!}.sup.-1                      (29)

is substantially constant over many samples of the measured signalsS.sub.λa and S.sub.λb. This assumption is accurate over many samplessince saturation generally changes relatively slowly in physiologicalsystems.

The constant saturation assumption is equivalent to assuming that:

    c.sub.5 (t)/c.sub.6 (t)=constant                           (30)

since the only other term in equation (29) is a constant, namely thenumeral 1.

Using this assumption, the proportionality constant ω_(s3) (t) whichallows determination of the noise reference signal n'(t) is: ##EQU5## Itis often the case that both equations (35) and (36) can besimultaneously satisfied to determine the proportionality constantω_(s3) (t). Additionally, the absorption coefficients at each wavelengthε₅,λa, ε₆,λa, ε₅,λb, and ε₆,λb are constant and the central assumptionof the constant saturation method is that c₅ (t)/c₆ (t) is constant overmany sample periods. Thus, a new proportionality constant ω_(s3) (t) maybe determined every few samples from new approximations to the desiredsignal as output from the adaptive noise canceler. Thus, theapproximations to the desired signals Y'.sub.λa (t) and Y'.sub.λb (t),found by the adaptive noise canceler for a substantially immediatelypreceding set of samples of the measured signals S.sub.λa (t) andS.sub.λb (t) are used in a processor of the present invention forcalculating the proportionality constant, ω_(s3) (t), for the next setof samples of the measured signals S.sub.λa (t) and S.sub.λb (t).

Multiplying equation (20) by ω_(s3) (t) and subtracting the resultingequation from equation (18) yields a non-zero noise reference signal:

    n'(t)=S.sub.λa (t)-ω.sub.s3 (t)S.sub.λb (t)=n.sub.λa (t)-ω.sub.s3 (t)n.sub.λb (t).(37)

It will be understood that equation (21) could be multiplied by ω_(s3)(t) and the resulting equation could be subtracted from equation (19) toyield the same noise reference signal n'(t) as given in equation (37).

When using the constant saturation method, it is necessary for thepatient to remain motionless for a short period of time such that anaccurate initial saturation value can be determined by known methodsother than adaptive noise canceling on which all other calculations willbe based. With no erratic, motion-induced undesired signal portions, aphysiological monitor can very quickly produce an initial value of thesaturation of A₅ in the volume containing A₅ and A₆. An example of asaturation calculation is given in the article "SPECTROPHOTOMETRICDETERMINATION OF OXYGEN SATURATION OF BLOOD INDEPENDENT OF THE PRESENTOF INDOCYANINE GREEN" by G. A. Mook, et al., wherein determination ofoxygen saturation in arterial blood is discussed. Another articlediscussing the calculation of oxygen saturation is "PULSE OXIMETRY:PHYSICAL PRINCIPLES, TECHNICAL REALIZATION AND PRESENT LIMITATIONS" byMichael R. Neuman. Then, with values for Y'.sub.λa (t) and Y'.sub.λb (t)determined, an adaptive noise canceler may be utilized with a noisereference signal n'(t) determined by the constant saturation method.

PREFERRED ADAPTIVE NOISE CANCELER USING A JOINT PROCESS ESTIMATORIMPLEMENTATION

Once the noise reference signal n'(t) is determined by the processor ofthe present invention using either the above described ratiometric orconstant saturation methods, the adaptive noise canceler can beimplemented in either hardware or software.

The least mean squares (LMS) implementation of the internal processor 32described above in conjunction with the adaptive noise canceler of FIG.5 is relatively easy to implement, but lacks the speed of adaptationdesirable for most physiological monitoring applications of the presentinvention. Thus, a faster approach for adaptive noise canceling, calleda least-squares lattice joint process estimator model, is preferablyused. A joint process estimator 60 is shown diagrammatically in FIG. 7and is described in detail in Chapter 9 of Adaptive Filter Theory bySimon Haykin, published by Prentice-Hall, copyright 1986. This entirebook, including Chapter 9, is hereby incorporated herein by reference.The function of the joint process estimator is to remove the undesiredsignal portions n.sub.λa (t) or n.sub.λb (t) from the measured signalsS.sub.λa (t) or S.sub.λb (t), yielding a signal Y'.sub.λa (t) orY'.sub.λb (t) which is a good approximation to the desired signalY.sub.λa (t) or Y.sub.λb (t). Thus, the joint process estimatorestimates the value of the desired signal Y.sub.λa (t) or Y.sub.λb (t).The inputs to the joint process estimator 60 are the noise referencesignal n'(t) and the composite measured signal S.sub.λa (t) or S.sub.λb(t). The output is a good approximation to the signal S.sub.λa (t) orS.sub.λb (t) with the noise removed, i.e. a good approximation toY.sub.λa (t) or Y.sub.λb (t).

The joint process estimator 60 utilizes, in conjunction, a least squarelattice predictor 70 and a regression filter 80. The noise referencesignal n'(t) is input to the least square lattice predictor 70 while themeasured signal S.sub.λa (t) or S.sub.λb (t) is input to the regressionfilter 80. For simplicity in the following description, S.sub.λa (t)will be the measured signal from which the desired portion Y.sub.λa (t)will be estimated by the joint process estimator 60. However, it will benoted that S.sub.λb (t) could equally well be input to the regressionfilter 80 and the desired portion Y.sub.λb (t) of this signal couldequally well be estimated.

The joint process estimator 60 removes all frequencies that are presentin both the noise reference signal n'(t) and the measured signalS.sub.λa (t). The undesired signal portion n.sub.λa (t) usuallycomprises frequencies unrelated to those of the desired signal portionY.sub.λa (t). It is highly improbable that the undesired signal portionn.sub.λa (t) would be of exactly the same spectral content as thedesired signal portion Y.sub.λa (t). However, in the unlikely event thatthe spectral content of S.sub.λa (t) and n'(t) are similar, thisapproach will not yield accurate results. Functionally, the jointprocess estimator 60 compares input signal n'(t), which is correlated tothe undesired signal portion n.sub.λa (t), and input signal S.sub.λa (t)and removes all frequencies which are identical. Thus, the joint processestimator 60 acts as a dynamic multiple notch filter to remove thosefrequencies in the undesired signal component n.sub.λa (t) as theychange erratically with the motion of the patient. This yields a signalhaving substantially the same spectral content as the desired signalY.sub.λa (t). The output of the joint process estimator 60 hassubstantially the same spectral content and amplitude as the desiredsignal Y.sub.λa (t). Thus, the output Y'.sub.λa (t) of the joint processestimator 60 is a very good approximation to the desired signal Y.sub.λa(t).

The joint process estimator 60 can be divided into stages, beginningwith a zero-stage and terminating in an m^(th) -stage, as shown in FIG.7. Each stage, except for the zero-stage, is identical to every otherstage. The zero-stage is an input stage for the joint process estimator60. The first stage through the m^(th) -stage work on the signalproduced in the immediately previous stage, i.e., the (m-1)^(th) -stage,such that a good approximation to the desired signal Y'.sub.λa (t) isproduced as output from the m^(th) -stage.

The least-squares lattice predictor 70 comprises registers 90 and 92,summing elements 100 and 102, and delay elements 110. The registers 90and 92 contain multiplicative values of a forward reflection coefficientΓ_(f),m (t) and a backward reflection coefficient Γ_(b),m (t) whichmultiply the noise reference signal n'(t) and signals derived from thenoise reference signal n'(t). Each stage of the least-squares latticepredictor outputs a forward prediction error f_(m) (t) and a backwardprediction error b_(m) (t). The subscript m is indicative of the stage.

For each set of samples, i.e. one sample of the noise reference signaln'(t) derived substantially simultaneously with one sample of themeasured signal S.sub.λa (t), the sample of the noise reference signaln'(t) is input to the least-squares lattice predictor 70. The zero-stageforward prediction error f₀ (t) and the zero-stage backward predictionerror b₀ (t) are set equal to the noise reference signal n'(t). Thebackward prediction error b₀ (t) is delayed by one sample period by thedelay element 110 in the first stage of the least-squares latticepredictor 70. Thus, the immediately previous value of the noisereference signal n'(t) is used in calculations involving the first-stagedelay element 110. The zero-stage forward prediction error is added tothe negative of the delayed zero-stage backward prediction error b₀(t-1) multiplied by the forward reflection coefficient value Γ_(f),1 (t)register 90 value, to produce a first-stage forward prediction error f₁(t). Additionally, the zero-stage forward prediction error f₀ (t) ismultiplied by the backward reflection coefficient value Γ_(b),1(t)register 92 value and added to the delayed zero-stage backwardprediction error b₀ (t-1) to produce a first-stage backward predictionerror b₁ (t). In each subsequent stage, m, of the least square latticepredictor 70, the previous forward and backward prediction error values,f_(m-1) (t) and b_(m-1) (t-1), the backward prediction error beingdelayed by one sample period, are used to produce values of the forwardand backward prediction errors for the present stage, f_(m) (t) andb_(m) (t).

The backward prediction error b_(m) (t) is fed to the concurrent stage,m, of the regression filter 80. There it is input to a register 96,which contains a multiplicative regression coefficient value κ_(m),λa(t). For example, in the zero-stage of the regression filter 80, thezero-stage backward prediction error b₀ (t) is multiplied by thezero-stage regression coefficient κ₀,λa (t) register 96 value andsubtracted from the measured value of the signal S.sub.λa (t) at asumming element 106 to produce a first stage estimation error signale₁,λa (t). The first-stage estimation error signal e₁,λa (t) is a firstapproximation to the desired signal. This first-stage estimation errorsignal e₂,λa (t) is input to the first-stage of the regression filter80. The first-stage backward prediction error b_(m) (t), multiplied bythe first-stage regression coefficient κ₁,λa (t) register 96 value issubtracted from the first-stage estimation error signal e₁,λa (t) toproduce the second-stage estimation error e₂,λa (t). The second-stageestimation error signal e₂,λa (t) is a second, somewhat betterapproximation to the desired signal Y.sub.λa (t).

The same processes are repeated in the least-squares lattice predictor70 and the regression filter 80 for each stage until a goodapproximation to the desired signal Y'.sub.λa (t)=e_(m),λa (t) isdetermined. Each of the signals discussed above, including the forwardprediction error f_(m) (t), the backward prediction error b_(m) (t), theestimation error signal e_(m),λa (t), is necessary to calculate theforward reflection coefficient Γ_(f),m (t), the backward reflectioncoefficient Γ_(b),m (t) and the regression coefficient κ_(m),λa (t)register 90, 92, and 96 values in each stage, m. In addition to theforward prediction error f_(m) (t), the backward prediction error b_(m)(t), and the estimation error e_(m),λa (t) signals, a number ofintermediate variables, not shown in FIG. 7 but based on the valueslabelled in FIG. 7, are required to calculate the forward reflectioncoefficient Γ_(f),m (t), the backward reflection coefficient Γ_(b),m(t), and the regression coefficient κ_(m),λa (t) register 90,92, and 96values.

Intermediate variables include a weighted sum of the forward predictionerror squares ℑ_(m) (t), a weighted sum of the backward prediction errorsquares β_(m) (t), a scaler parameter Δ_(m) (t), a conversion factorγ_(m) (t), and another scaler parameter ρ_(m),λa (t). The weighted sumof the forward prediction errors ℑ_(m) (t) is defined as: ##EQU6## whereλ without a wavelength identifier, a or b, is a constant multiplicativevalue unrelated to wavelength and is typically less than or equal toone, i.e., λ≦1. The weighted sum of the backward prediction errors β_(m)(t) is defined as: ##EQU7## where, again, λ without a wavelengthidentifier, a or b, is a constant multiplicative value unrelated towavelength and is typically less than or equal to one, i.e., λ≦1. Theseweighted sum intermediate error signals can be manipulated such thatthey are more easily solved for, as described in Chapter 9, §9.3. anddefined hereinafter in equations (53) and (54).

DESCRIPTION OF THE JOINT PROCESS ESTIMATOR

The operation of the joint process estimator 60 is as follows. When thejoint process estimator 60 is turned on, the initial values ofintermediate variable and signal including the parameter Δ_(m-1) (t),the weighted sum of the forward prediction error signals ℑ_(m-1) (t),the weighted sum of the backward prediction error signals β_(m-1) (t),the parameter ρ_(m),λa (t), and the zero-stage estimation error e₀,λa(t) are initialized, some to zero and some to a small positive number δ:

    Δ.sub.m-1 (0)=0;                                     (40)

    ℑ.sub.m-1 (0)=δ;                             (41)

    β.sub.m-1 (0)=δ;                                (42)

    ρ.sub.m,λa (0)=0;                               (43)

    e.sub.0,λa (t)=S.sub.λa (t) for t≧0.  (44)

After initialization, a simultaneous sample of the measured signalS.sub.λa (t) and the noise reference signal n'(t) are input to the jointprocess estimator 60, as shown in FIG. 7. The forward and backwardprediction error signals f₀ (t) and b₀ (t), and intermediate variablesincluding the weighted sums of the forward and backward error signals ℑ₀(t) and β₀ (t), and the conversion factor γ₀ (t) are calculated for thezero-stage according to:

    f.sub.0 (t)=b.sub.0 (t)=n'(t)                              (45)

    ℑ.sub.0 (t)=β.sub.0 (t)=λℑ.sub.0 (t-1)+|n'(t)|.sup.2                     (46)

    γ.sub.0 (t-1)=1                                      (47)

where, again, λ without a wavelength identifier, a or b, is a constantmultiplicative value unrelated to wavelength.

Forward reflection coefficient Γ_(f),m (t), backward reflectioncoefficient Γ_(b),m (t), and regression coefficient κ_(m),λa (t)register 90, 92 and 96 values in each stage thereafter are set accordingto the output of the previous stage. The forward reflection coefficientΓ_(f),1 (t), backward reflection coefficient Γ_(b),1 (t), and regressioncoefficient κ₁,λa (t) register 90, 92 and 96 values in the first stageare thus set according to algorithm using values in the zero-stage ofthe joint process estimator 60. In each stage, m≧1, intermediate valuesand register values including the parameter Δ_(m-1) (t); the forwardreflection coefficient Γ_(f),m (t) register 90 value; the backwardreflection coefficient Γ_(b),m (t) register 92 value; the forward andbackward error signals f_(m) (t) and b_(m) (t); the weighted sum ofsquared forward prediction errors ℑ_(f),m (t) as manipulated in §9.3 ofthe Haykin book; the weighted sum of squared backward prediction errorsβ_(b),m (t), as manipulated in §9.3 of the Haykin book; the conversionfactor γ_(m) (t); the parameter ρ_(m),λa (t); the regression coefficientκ_(m),λa (t) register 96 value; and the estimation error e_(m+1),λa (t)value are set according to:

    Δ.sub.m-1 (t)=λΔ.sub.m-1 (t-1)+{b.sub.m-1 (t-1)f*.sub.m-1 (t)/γ.sub.m-1 (t-1)}                (48)

    Γ.sub.f,m (t)=-{Δ.sub.m-1 (t)/β.sub.m-1 (t-1)}(49)

    Γ.sub.b,m (t)=-{Δ*.sub.m-1 (t)/ℑ.sub.m-1 (t)}(50)

    f.sub.m (t)=f.sub.m-1 (t)+Γ*.sub.f,m (t)b.sub.m-1 (t-1)(51)

    b.sub.m (t)=b.sub.m-1 (t-1)+Γ*.sub.b,m (t)f.sub.m-1 (t)(52)

    ℑ.sub.m (t)=ℑ.sub.m-1 (t)-{|Δ.sub.m-1 (t)|.sup.2 /β.sub.m-1 (t-1)}                (53)

    β.sub.m (t)=β.sub.m-1 (t-1)-{|Δ.sub.m-1 (t)|.sup.2 /ℑ.sub.m-1 (t)}               (54)

    γ.sub.m (t-1)=γ.sub.m-1 (t-1)-{|b.sub.m-1 (t-1)|.sup.2 /β.sub.m-1 (t-1)}              (55)

    ρ.sub.m,λa (t)=λρ.sub.m,λa (t-1)+{b.sub.m (t)e*.sub.m,λa (t)/γ.sub.m (t)}              (56)

    κ.sub.m,λa (t)={ρ.sub.m,λa (t)/β.sub.m (t)}(57)

    e.sub.m+1,λa (t)=e.sub.m,λa (t)-κ*.sub.m (t)b.sub.m (t)(58)

where a (*) denotes a complex conjugate.

These equations cause the error signals f_(m) (t), b_(m) (t), e_(m),λa(t) to be squared or to be multiplied by one another, in effect squaringthe errors, and creating new intermediate error values, such as Δ_(m-1)(t). The error signals and the intermediate error values are recursivelytied together, as shown in the above equations (48) through (58). Theyinteract to minimize the error signals in the next stage.

After a good approximation to the desired signal Y'.sub.λa (t) has beendetermined by the joint process estimator 60, a next set of samples,including a sample of the measured signal S.sub.λa (t) and a sample ofthe noise reference signal n'(t), are input to the joint processestimator 60. The reinitialization process does not re-occur, such thatthe forward and backward reflection coefficient Γ_(f),m (t) and Γ_(b),m(t) register 90, 92 values and the regression coefficient K_(m),λa (t)register 96 value reflect the multiplicative values required to estimatethe desired portion Y.sub.λa (t) of the sample of S.sub.λa (t) inputpreviously. Thus, information from previous samples is used to estimatethe desired signal portion of a present set of samples in each stage.

FLOWCHART OF JOINT PROCESS ESTIMATOR

In a signal processor, such as a physiological monitor, incorporating areference processor of the present invention to determine a noisereference signal n'(t) for input to an adaptive noise canceler, a jointprocess estimator 60 type adaptive noise canceler is generallyimplemented via a software program having an iterative loop. Oneiteration of the loop is analogous to a single stage of the jointprocess estimator as shown in FIG. 7. Thus, if a loop is iterated mtimes, it is equivalent to an m stage joint process estimator 60.

A flow chart of a subroutine to estimate the desired signal portionY.sub.λa (t) of a sample of a measured signal, S.sub.λa (t) is shown inFIG. 8. The flow chart describes how the action of a reference processorfor determining the noise reference signal and the joint processestimator 60 would be implemented in software.

A one-time only initialization is performed when the physiologicalmonitor is turned on, as indicated by an "INITIALIZE NOISE CANCELER" box120. The initialization sets all registers 90, 92, and 96 and delayelement variables 110 to the values described above in equations (40)through (44).

Next, a set of simultaneous samples of the measured signals S.sub.λa (t)and S.sub.λb (t) is input to the subroutine represented by the flowchartin FIG. 8. Then a time update of each of the delay element programvariables occurs, as indicated in a "TIME UPDATE OF Z⁻¹ ! ELEMENTS" box130, wherein the value stored in each of the delay element variables 110is set to the value at the input of the delay element variable 110.Thus, the zero-stage backward prediction error b₀ (t) is stored in thefirst-stage delay element variable, the first-stage backward predictionerror b₁ (t) is stored in the second-stage delay element variable, andso on.

Then, using the set of measured signal samples S.sub.λa (t) and S.sub.λb(t), the noise reference signal is calculated according to theratiometric or the constant saturation method described above. This isindicated by a "CALCULATE NOISE REFERENCE (n'(t)) FOR TWO MEASUREDSIGNAL SAMPLES" box 140. The ratiometric method is generally preferredsince no assumptions about constant saturation values need be made.

A zero-stage order update is performed next as indicated in a"ZERO-STAGE UPDATE" box 150. The zero-stage backward prediction error b₀(t), and the zero-stage forward prediction error f₀ (t) are set equal tothe value of the noise reference signal n'(t). Additionally, theweighted sum of the forward prediction errors ℑ_(m) (t) and the weightedsum of backward prediction errors β_(m) (t) are set equal to the valuedefined in equation (46).

Next, a loop counter, m, is initialized as indicated in a "m=0" box 160.A maximum value of m, defining the total number of stages to be used bythe subroutine corresponding to the flowchart in FIG. 8, is alsodefined. Typically, the loop is constructed such that it stops iteratingonce a criterion for convergence upon a best approximation to thedesired signal has been met by the joint process estimator 60.Additionally, a maximum number of loop iterations may be chosen at whichthe loop stops iteration. In a preferred embodiment of a physiologicalmonitor of the present invention, a maximum number of iterations, m=60to m=80, is advantageously chosen.

Within the loop, the forward and backward reflection coefficient Γ_(f),m(t) and Γ_(b),m (t) register 90 and 92 values in the least-squareslattice filter are calculated first, as indicated by the "ORDER UPDATEMTH CELL OF LSL-LATTICE" box 170 in FIG. 8. This requires calculation ofintermediate variable and signal values used in determining register 90,92, and 96 values in the present stage, the next stage, and in theregression filter 80

The calculation of regression filter register 96 value κ_(m),λa (t) isperformed next, indicated by the "ORDER UPDATE MTH STAGE OF REGRESSIONFILTER(S)" box 180. The two order update boxes 170 and 180 are performedin sequence m times, until m has reached its predetermined maximum (inthe preferred embodiment, m=60 to m=80) or a solution has been convergedupon, as indicated by a YES path from a "DONE" decision box 190. In acomputer subroutine, convergence is determined by checking if theweighted sums of the forward and backward prediction errors ℑ_(m) (t)and β_(m) (t) are less than a small positive number. An output iscalculated next, as indicated by a "CALCULATE OUTPUT" box 200. Theoutput is a good approximation to the desired signal, as determined bythe reference processor and joint process estimator 60 subroutinecorresponding to the flow chart of FIG. 8. This is displayed (or used ina calculation in another subroutine), as indicated by a "TO DISPLAY" box210.

A new set of samples of the two measured signals S.sub.λa (t) andS.sub.λb (t) is input to the processor and joint process estimator 60adaptive noise canceler subroutine corresponding to the flowchart ofFIG. 8 and the process reiterates for these samples. Note, however, thatthe initialization process does not re-occur. New sets of measuredsignal samples S.sub.λa (t) and S.sub.λb (t) are continuously input tothe reference processor and joint process estimator 60 adaptive noisecanceler subroutine. The output forms a chain of samples which isrepresentative of a continuous wave. This waveform is a goodapproximation to the desired signal waveform Y'.sub.λa (t) at wavelengthλa.

CALCULATION OF SATURATION FROM ADAPTIVE NOISE CANCELER OUTPUT

Physiological monitors typically use the approximation of the desiredsignal Y'.sub.λa (t) to calculate another quantity, such as thesaturation of one constituent in a volume containing that constituentplus one or more other constituents. Generally, such calculationsrequire information about a desired signal at two wavelengths. In somemeasurements, this wavelength is λb, the wavelength used in thecalculation of the noise reference signal n'(t). For example, theconstant saturation method of determining the noise reference signalrequires a good approximation of the desired signal portions Y.sub.λa(t) and Y.sub.λb (t) of both measured signals S.sub.λa (t) and S.sub.λb(t). Then, the saturation is determined from the approximations to bothsignals, i.e. Y'.sub.λa (t) and Y'.sub.λb (t).

In other physiological measurements, information about a signal at athird wavelength is necessary. For example, to find the saturation usingthe ratiometric method, signals S.sub.λa (t) and S.sub.λb (t) are usedto find the noise reference signal n'(t). But as discussed previously,λa and λb were chosen to satisfy a proportionality relationship likethat of equation (22). This proportionality relationship forces the twodesired signal portions Y.sub.λa (t) and Y.sub.λb (t) to be linearlydependant. Generally, linearly dependant mathematical equations cannotbe solved for the unknowns. Analogously, some desirable informationcannot be derived from two linearly dependent signals. Thus, todetermine the saturation using the ratiometric method, a third signal issimultaneously measured at wavelength λc. The wavelength Ac is chosensuch that the desired portion Y.sub.λc (t) of the measured signalS.sub.λc (t) is not linearly dependent with the desired portionsY.sub.λa (t) and Y.sub.λb (t) of the measured signals S.sub.λa (t) andS.sub.λb (t). Since all measurements are taken substantiallysimultaneously, the noise reference signal n'(t) is correlated to theundesired signal portions n.sub.λa, n.sub.λb, and n.sub.λc of each ofthe measured signals S.sub.λa (t), S.sub.λb (t), and S.sub.λc (t) andcan be used to estimate approximations to the desired signal portionsY.sub.λa (t), Y.sub.λb (t), and Y.sub.λc (t) for all three measuredsignals S.sub.λa (t), S.sub.λb (t), and S.sub.λc (t). Using theratiometric method, estimation of the desired signal portions Y.sub.λa(t) and Y.sub.λc (t) of two measured signals S.sub.λa (t) and S.sub.λc(t), chosen correctly, is usually satisfactory to determine mostphysiological data.

A joint process estimator 60 having two regression filters 80a and 80bis shown in FIG. 9. A first regression filter 80a accepts a measuredsignal S.sub.λa (t). A second regression filter 80b accepts a measuredsignal S.sub.λb (t) or S.sub.λc (t), depending whether the constantsaturation method or the ratiometric method is used to determine thenoise reference signal n'(t). The first and second regression filters80a and 80b are independent. The backward prediction error b_(m) (t) isinput to each regression filter 80a and 80b, the input for the secondregression filter 80b bypassing the first regression filter 80a.

The second regression filter 80b comprises registers 98, and summingelements 108 arranged similarly to those in the first regression filter80a. The second regression filter 80b operates via an additionalintermediate variable in conjunction with those defined by equations(48) through (58), i.e.:

    ρ.sub.m,λb (t)=λρ.sub.m,λb (t-1)+{b.sub.m (t)e*.sub.m,λb (t)/γ.sub.m (t)};             (59)

or

    ρ.sub.m,λc (t)=λρ.sub.m,λc (t-1)+{b.sub.m (t)e*.sub.m,λc (t)/γ.sub.m (t)};             (60)

and

    ρ.sub.0,λb (0)=0;                               (61)

or

    ρ.sub.0,λc (0)=0.                               (62)

The second regression filter 80b has an error signal value definedsimilar to the first regression filter error signal values, e_(m+1),λa(t), i.e.:

    e.sub.m+1,λb (t)=e.sub.m,λb (t)-κ*.sub.m,λb (t)b.sub.m (t);                                           (63)

or

    e.sub.m+1,λc (t)=e.sub.m,λc (t)-κ*.sub.m,λb (t)b.sub.m (t);                                           (64)

and

    e.sub.0,λb (t)=S.sub.λb (t) for t≧0;  (65)

or

    e.sub.0,λc (t)=S.sub.λc (t) for t≧0.  (66)

The second regression filter has a regression coefficient κ_(m),λb (t)register 98 value defined similarly to the first regression filter errorsignal values, i.e.:

    κ.sub.m,λb (t)={ρ.sub.m,λb (t)/β.sub.m (t)};(67)

or

    κ.sub.m,λc (t)={ρ.sub.m,λc (t)/β.sub.m (t)};(68)

These values are used in conjunction with those intermediate variablevalues, signal values, register and register values defined in equations(40) through (58). These signals are calculated in an order defined byplacing the additional signals immediately adjacent a similar signal forthe wavelength λa.

For the ratiometric method, S.sub.λc (t) is input to the secondregression filter 80b. The output of the second regression filter 80b isthen a good approximation to the desired signal Y'.sub.λc (t). For theconstant saturation method, S.sub.λb (t) is input to the secondregression filter 80b. The output is then a good approximation to thedesired signal Y'.sub.λb (t).

The addition of the second regression filter 80b does not substantiallychange the computer program subroutine represented by the flowchart ofFIG. 8. Instead of an order update of the mth stage of only oneregression filter, an order update of the m^(th) stage of bothregression filters 80a and 80b is performed. This is characterized bythe plural designation in the "ORDER UPDATE OF m^(th) STAGE OFREGRESSION FILTER(S)" box 180 in FIG. 8. Since the regression filters80a and 80b operate independently, independent calculations can beperformed in the reference processor and joint process estimator 60adaptive noise canceler subroutine modeled by the flowchart of FIG. 8.

CALCULATION OF SATURATION

Once good approximations to the desired signals, Y'.sub.λa (t) andY'.sub.λc (t) for the ratiometric method and Y'.sub.λa (t) and Y'.sub.λb(t) for the constant saturation method, have been determined by thejoint process estimator 60, the saturation of A₅ in a volume containingA₅ and A₆, for example, may be calculated according to various knownmethods. Mathematically, the approximations to the desired signals canbe written:

    Y'.sub.λa (t)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t);      (69)

and

    Y'.sub.λc (t)≈ε.sub.5,λc c.sub.5 x.sub.5,6 (t)+ε.sub.6,λc c.sub.6 x.sub.5,6 (t).      (70)

for the ratiometric method using wavelengths λa and λc. For the constantsaturation method, the approximations to the desired signals can bewritten in terms of λa and λb as:

    Y'.sub.λa (t)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t);      (71)

and

    Y'.sub.λb (t)≈ε.sub.5,λb c.sub.5 x.sub.5,6 (t)+ε.sub.6,λb c.sub.6 x.sub.5,6 (t).      (72)

This is equivalent to two equations having three unknowns, namely c₅(t), c₆ (t) and x₅,6 (t). In both the ratiometric and the constantsaturation cases, the saturation can be determined by acquiringapproximations to the desired signal portions at two different, yetproximate times t₁ and t₂ over which the saturation of A₅ in the volumecontaining A₅ and A₆ does not change substantially. For example, for thedesired signals estimated by the ratiometric method, at times t₁ and t₂:

    Y'.sub.λa (t.sub.1)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t.sub.1)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t.sub.1).(73)

    Y'.sub.λc (t.sub.1)≈ε.sub.5,λc c.sub.5 x.sub.5,6 (t.sub.1)+ε.sub.6,λc c.sub.6 x.sub.5,6 (t.sub.1)(74)

    Y'.sub.λa (t.sub.2)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t.sub.2)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t.sub.2)(75)

    Y'.sub.λc (t.sub.2)≈ε.sub.5,λc c.sub.5 x.sub.5,6 (t.sub.2)+ε.sub.6,λc c.sub.6 x.sub.5,6 (t.sub.2)(76)

Then, difference signals may be determined which relate the signals ofequation (73) through (76), i.e.:

    ΔY.sub.λa =Y'.sub.λa (t.sub.1)-Y'.sub.λa (t.sub.2)≈ε.sub.5,λa c.sub.5 Δx+ε.sub.6,λa c.sub.6 Δx;      (77)

and

    ΔY.sub.λc =Y'.sub.λc (t.sub.1)-Y'.sub.λc (t.sub.2)≈ε.sub.5,λc c.sub.5 Δx+ε.sub.6,λc c.sub.6 Δx;      (78)

where Δx=x₅,6 (t₁)-x₅,6 (t₂). The average saturation at time t=(t₁+t₂)/2 is: ##EQU8## It will be understood that the Ax term drops outfrom the saturation calculation because of the division. Thus, knowledgeof the thickness of the desired constituents is not required tocalculate saturation.

PULSE OXIMETRY MEASUREMENTS

A specific example of a physiological monitor utilizing a processor ofthe present invention to determine a noise reference signal n'(t) forinput to an adaptive noise canceler that removes erratic motion-inducedundesired signal portions is a pulse oximeter. A pulse oximetertypically causes energy to propagate through a medium where blood flowsclose to the surface for example, an ear lobe, or a digit such as afinger, or a forehead. An attenuated signal is measured afterpropagation through or reflection from the medium. The pulse oximeterestimates the saturation of oxygenated blood available to the body foruse.

Freshly oxygenated blood is pumped at high pressure from the heart intothe arteries for use by the body. The volume of blood in the arteriesvaries with the heartbeat, giving rise to a variation in absorption ofenergy at the rate of the heartbeat, or the pulse.

Oxygen depleted, or deoxygenated, blood is returned to the heart by theveins along with unused oxygenated blood. The volume of blood in theveins varies with the rate of breathing, which is typically much slowerthan the heartbeat. Thus, when there is no motion induced variation inthe thickness of the veins, venous blood causes a low frequencyvariation in absorption of energy. When there is motion inducedvariation in the thickness of the veins, the low frequency variation inabsorption is coupled with the erratic variation in absorption due tomotion artifact.

In absorption measurements using the transmission of energy through amedium, two light emitting diodes (LED's) are positioned on one side ofa portion of the body where blood flows close to the surface, such as afinger, and a photodetector is positioned on the opposite side of thefinger. Typically, in pulse oximetry measurements, one LED emits avisible wavelength, preferably red, and the other LED emits an infraredwavelength. However, one skilled in the art will realize that otherwavelength combinations could be used.

The finger comprises skin, tissue, muscle, both arterial blood andvenous blood, fat, etc., each of which absorbs light energy differentlydue to different absorption coefficients, different concentrations, anddifferent thicknesses. When the patient is not moving, absorption issubstantially constant except for the flow of blood. This constantattenuation can be determined and subtracted from the signal viatraditional filtering techniques. When the patient moves, the absorptionbecomes erratic. Erratic motion induced noise typically cannot bepredetermined and subtracted from the measured signal via traditionalfiltering techniques. Thus, determining the saturation of oxygenatedarterial blood becomes more difficult.

A schematic of a physiological monitor for pulse oximetry is shown inFIG. 10. Two LED's 300 and 302, one LED 300 emitting red wavelengths andanother LED 302 emitting infrared wavelengths, are placed adjacent afinger 310. A photodetector 320, which produces an electrical signalcorresponding to the attenuated visible and infrared light energysignals is located opposite the LED's 300 and 302. The photodetector 320is connected to a single channel of common processing circuitryincluding an amplifier 330 which is in turn connected to a band passfilter 340. The band pass filter 340 passes signal into a synchronizeddemodulator 350 which has a plurality of output channels. One outputchannel is for signals corresponding to visible wavelengths and anotheroutput channel is for signals corresponding to infrared wavelengths.

The output channels of the synchronized demodulator for signalscorresponding to both the visible and infrared wavelengths are eachconnected to separate paths, each path comprising further processingcircuitry. Each path includes a DC offset removal element 360 and 362,such as a differential amplifier, a programmable gain amplifier 370 and372 and a low pass filter 380 and 382. The output of each low passfilter 380 and 382 is amplified in a second programmable gain amplifier390 and 392 and then input to a multiplexer 400.

The multiplexer 400 is connected to an analog-to-digital converter 410which is in turn connected to a microprocessor 420. Control linesbetween the microprocessor 420 and the multiplexer 400, themicroprocessor 420 and the analog-to-digital converter 410, and themicroprocessor 420 and each programmable gain amplifier 370, 372, 390,and 392 are formed. The microprocessor 420 has additional control lines,one of which leads to a display 430 and the other of which leads to anLED driver 440 situated in a feedback loop with the two LED's 300 and302.

The LED's 300 and 302 each emits energy which is absorbed by the finger310 and received by the photodetector 320. The photodetector 320produces an electrical signal which corresponds to the intensity of thelight energy striking the photodetector 320 surface. The amplifier 330amplifies this electrical signal for ease of processing. The band passfilter 340 then removes unwanted high and low frequencies. Thesynchronized demodulator 350 separates the electrical signal intoelectrical signals corresponding to the red and infrared light energycomponents. A predetermined reference voltage, V_(ref), is subtracted bythe DC offset removal element 360 and 362 from each of the separatesignals to remove substantially constant absorption which corresponds toabsorption when there is no motion induced undesired signal component.Then the first programmable gain amplifiers 370 and 372 amplify eachsignal for ease of manipulation. The low pass filters 380 and 382integrate each signal to remove unwanted high frequency components andthe second programmable gain amplifiers 390 and 392 amplify each signalfor further ease of processing.

The multiplexer 400 acts as an analog switch between the electricalsignals corresponding to the red and the infrared light energy, allowingfirst a signal corresponding to the red light to enter theanalog-to-digital convertor 410 and then a signal corresponding to theinfrared light to enter the analog-to-digital convertor 410. Thiseliminates the need for multiple analog-to-digital convertors 410. Theanalog-to-digital convertor 410 inputs the data into the microprocessor420 for calculation of a noise reference signal via the processingtechnique of the present invention and removal of undesired signalportions via an adaptive noise canceler. The microprocessor 420centrally controls the multiplexer 400, the analog-to-digital convertor410, and the first and second programmable gain amplifiers 370 and 390for both the red and the infrared channels. Additionally, themicroprocessor 420 controls the intensity of the LED's 302 and 304through the LED driver 440 in a servo loop to keep the average intensityreceived at the photodetector 320 within an appropriate range. Withinthe microprocessor 420 a noise reference signal n'(t) is calculated viaeither the constant saturation method or the ratiometric method, asdescribed above, the ratiometric method being generally preferred. Thissignal is used in an adaptive noise canceler of the joint processestimator type 60, described above.

The multiplexer 400 time multiplexes, or sequentially switches between,the electrical signals corresponding to the red and the infrared lightenergy. This allows a single channel to be used to detect and beginprocessing the electrical signals. For example, the red LED 300 isenergized first and the attenuated signal is measured at thephotodetector 320. An electrical signal corresponding to the intensityof the attenuated red light energy is passed to the common processingcircuitry. The infrared LED 302 is energized next and the attenuatedsignal is measured at the photodetector 320. An electrical signalcorresponding to the intensity of the attenuated infrared light energyis passed to the common processing circuitry. Then, the red LED 300 isenergized again and the corresponding electrical signal is passed to thecommon processing circuitry. The sequential energization of LED's 300and 302 occurs continuously while the pulse oximeter is operating.

The processing circuitry is divided into distinct paths after thesynchronized demodulator 350 to ease time constraints generated by timemultiplexing. In the preferred embodiment of the pulse oximeter shown inFIG. 10, a sample rate, or LED energization rate, of 1000 Hz isadvantageously employed. Thus, electrical signals reach the synchronizeddemodulator 350 at a rate of 1000 Hz. Time multiplexing is not used inplace of the separate paths due to settling time constraints of the lowpass filters 380, 382, and 384.

In FIG. 10, a third LED 304 is shown adjacent the finger, located nearthe LED's 300 and 302. The third LED 304 is used to measure a thirdsignal S.sub.λc (t) to be used to determine saturation using theratiometric method. The third LED 304 is time multiplexed with the redand infrared LED's 300 and 302. Thus, a third signal is input to thecommon processing circuitry in sequence with the signals from the redand infrared LED's 300 and 302. After passing through and beingprocessed by the operational amplifier 330, the band pass filter 340,and the synchronized demodulator 350, the third electrical signalcorresponding to light energy at wavelength Ac is input to a separatepath including a DC offset removal element 364, a first programmablegain amplifier 374, a low pass filter 384, and a second programmablegain amplifier 394. The third signal is then input to the multiplexer400.

The dashed line connection for the third LED 304 indicates that thisthird LED 304 is incorporated into the pulse oximeter when theratiometric method is used; it is unnecessary for the constantsaturation method. When the third LED 304 is used, the multiplexer 400acts as an analog switch between all three LED 300, 302, and 304signals. If the third LED 304 is utilized, feedback loops between themicroprocessor 420 and the first and second programmable gain amplifier374 and 394 in the λc wavelength path are also formed.

For pulse oximetry measurements using the ratiometric method, thesignals (logarithm converted) transmitted through the finger 310 at eachwavelength λa, λb, and λc are:

    S.sub.λa (t)=S.sub.λred1 (t)=ε.sub.HbO2,λa c.sub.HbO2.sup.A x.sup.A (t)+ε.sub.Hb,λa c.sub.Hb.sup.A x.sup.A (t)+ε.sub.HbO2,λa c.sub.HbO2.sup.V x.sup.V (t)+ε.sub.Hb,λa c.sub.Hb.sup.V x.sup.V (t)+n.sub.λa (t).                                                      (81)

    S.sub.λb (t)=S.sub.λred2 (t)=ε.sub.HbO2,λb c.sub.HbO2.sup.A x.sup.A (t)+ε.sub.Hb,λb c.sub.Hb.sup.A x.sup.A (t)+ε.sub.HbO2,λb c.sub.HbO2.sup.V x.sup.V (t)+ε.sub.Hb,λb c.sub.Hb.sup.V x.sup.V (t)+n.sub.λb (t).                                                      (82)

    S.sub.λc (t)=S.sub.λIR (t)=ε.sub.HbO2,λc c.sub.HbO2.sup.A x.sup.A (t)+ε.sub.Hb,λc c.sub.Hb.sup.A x.sup.A (t)+ε.sub.HbO2,λc c.sub.HbO2.sup.V x.sup.V (t)+ε.sub.Hb,λc c.sub.Hb.sup.V x.sup.V +n.sub.λc (t).(83)

In equations (81) through (83), x^(A) (t) is the lump-sum thickness ofthe arterial blood in the finger; x^(V) (t) is the lump-sum thickness ofvenous blood in the finger; ε_(HbO2),λa ε_(HbO2),λb, ε_(HbO2),λc,ε_(Hb),λa, ε_(Hb),λb, and ε_(Hb),λc are the absorption coefficients ofthe oxygenated and non-oxygenated hemoglobin, at each wavelengthmeasured; and c_(HbO2) (t) and c_(Hb) (t) with the superscriptdesignations A and V are the concentrations of the oxygenated andnon-oxygenated arterial blood and venous blood, respectively.

For the ratiometric method, the wavelengths chosen are typically two inthe visible red range, i.e., λa and λb, and one in the infrared range,i.e., λc. As described above, the measurement wavelengths λa and λb areadvantageously chosen to satisfy a proportionality relationship whichremoves the desired signal portion Y.sub.λa (t) and Y.sub.λb (t),yielding a noise reference signal n'(t). In the preferred embodiment,the ratiometric method is used to determine the noise reference signaln'(t) by picking two wavelengths that cause the desired portionsY.sub.λa (t) and Y.sub.λb (t) of the measured signals S.sub.λa (t) andS.sub.λb (t) to become linearly dependent similarly to equation (22);i.e. wavelengths λa and λb which satisfy:

    ε.sub.HbO2,λa /ε.sub.Hb,λa =ε.sub.HbO2,λb /ε.sub.HB,λb (84)

Typical wavelength values chosen are λa=650 nm and λb=685 nm.Additionally a typical wavelength value for λc is λc=940 nm. By pickingwavelengths λa and λb to satisfy equation (84) the venous portion of themeasured signal is also caused to become linearly dependent even thoughit is not a portion of the desired signal. Thus, the venous portion ofthe signal is removed with the desired portion. The proportionalityrelationship between equations (81) and (82) which allows determinationof a non-zero noise reference signal n'(t), similarly to equation (25)is:

    ω.sub.r4 =ε.sub.Hb,λa /ε.sub.Hb,λb ;(85)

where

    n.sub.λa (t)≠ω.sub.r4 n.sub.λb (t).(86)

In pulse oximetry, both equations (85) and (86) can typically besatisfied simultaneously.

FIG. 11 is a graph of the absorption coefficients of oxygenated anddeoxygenated hemoglobin (ε_(HbO2) and ε_(Hb)) vs. wavelength (λ). FIG.12 is a graph of the ratio of absorption coefficients vs. wavelength,i.e., ε_(Hb) /ε_(HbO2) vs. λ over the range of wavelength within circle13 in FIG. 11. Anywhere a horizontal line touches the curve of FIG. 12twice, as does line 400, the condition of equation (84) is satisfied.FIG. 13 shows an exploded view of the area of FIG. 11 within the circle13. Values of ε_(HbO2) and ε_(Hb) at the wavelengths where a horizontalline touches the curve of FIG. 12 twice can then be determined from thedata in FIG. 13 to solve for the proportionality relationship ofequation (85).

A special case of the ratiometric method is when the absorptioncoefficients ε_(HbO2) and ε_(Hb) are equal at a wavelength. Arrow 410 inFIG. 11 indicates one such location, called an isobestic point. FIG. 13shows an exploded view of the isobestic point. To use isobestic pointswith the ratiometric method, two wavelengths at isobestic points aredetermined to satisfy equation (84).

Multiplying equation (82) by ω_(r4) and then subtracting equation (82)from equation (81), a non-zero noise reference signal n'(t) isdetermined by:

    n'(t)=S.sub.λa (t)-ω.sub.r4 S.sub.λb (t)=n.sub.λa (t)-ω.sub.r4 n.sub.λb.   (87)

This noise reference signal n'(t) has spectral content corresponding tothe erratic, motion-induced noise. When it is input to an adaptive noisecanceler, with either the signals S.sub.λa (t) and S.sub.λc (t) orS.sub.λb (t) and S.sub.λc (t) input to two regression filters 80a and80b, the adaptive noise canceler will function much like an adaptivemultiple notch filter and remove frequency components present in boththe noise reference signal n'(t) and the measured signals from themeasured signals S.sub.λa (t) and S.sub.λc (t) or S.sub.λb (t) andS.sub.λc (t). Thus, the adaptive noise canceler is able to removeerratic noise caused in the venous portion of the measured signalsS.sub.λa (t), S.sub.λb (t), and S.sub.λc (t) even though the venousportion of the measured signals S.sub.λa (t) and S.sub.λb (t) was notincorporated in the noise reference signal n'(t). However, the lowfrequency absorption caused by venous blood moving through the veins isgenerally not one of the frequencies incorporated into the noisereference signal n'(t). Thus, the adaptive noise canceler generally willnot remove this portion of the undesired signal. However, a band passfilter applied to the approximations to the desired signals Y'.sub.λa(t) and Y'.sub.λc (t) or Y'.sub.λb (t) and Y'.sub.λc (t) can remove thisportion of the undesired signal corresponding to the low frequencyvenous absorption.

For pulse oximetry measurements using the constant saturation method,the signals (logarithm converted) transmitted through the finger 310 ateach wavelength λa and λb are:

    S.sub.λa (t)=S.sub.λred1 (t)=ε.sub.HbO2,λa c.sub.HbO2.sup.A x.sup.A (t)+ε.sub.Hb,λa c.sub.Hb.sup.A x.sup.A (t)+ε.sub.HbO2,λa c.sub.HbO2.sup.V x.sup.V (t)+ε.sub.Hb,λa c.sub.Hb.sup.V x.sup.V (t)+n.sub.λa (t).                                                      (88)

    S.sub.λb (t)=S.sub.λIR (t)=ε.sub.HbO2,λb c.sub.HbO2.sup.A x.sup.A (t)+ε.sub.Hb,λb c.sub.Hb.sup.A x.sup.A (t)+ε.sub.HbO2,λb c.sub.HbO2.sup.V x.sup.V (t)+ε.sub.Hb,λb c.sub.Hb.sup.V x.sup.V (t)+n.sub.λb (t).                                                      (89)

For the constant saturation method, the wavelengths chosen are typicallyone in the visible red range, i.e., λa, and one in the infrared range,i.e., λb. Typical wavelength values chosen are λa=660 nm and λb=940 nm.Using the constant saturation method, it is assumed that c_(HbO2)(t)/c_(Hb) (t)=constant. The saturation of oxygenated arterial bloodchanges slowly, if at all, with respect to the sample rate, making thisa valid assumption. The proportionality factor between equation (88) and(89) can then be written as: ##EQU9## In pulse oximetry, it is typicallythe case that both equations (91) and (92) can be satisfiedsimultaneously.

Multiplying equation (89) by ω_(s4) (t) and then subtracting equation(89) from equation (88), a non-zero noise reference signal n'(t) isdetermined by: ##EQU10##

The constant saturation assumption does not cause the venouscontribution to the absorption to be canceled along with the desiredsignal portions Y.sub.λa (t) and Y.sub.λb (t), as did the relationshipof equation (84) used in the ratiometric method. Thus, frequenciesassociated with both the low frequency modulated absorption due tovenous absorption when the patient is still and the erraticallymodulated absorption due to venous absorption when the patient is movingare represented in the noise reference signal n'(t). Thus, the adaptivecanceler can remove both erratically modulated absorption due to venousblood in the finger under motion and the constant low frequency cyclicabsorption of venous blood.

Using either method, a noise reference signal n'(t) is determined by theprocessor of the present invention for use in an adaptive noise cancelerwhich is defined by software in the microprocessor. The preferredadaptive noise canceler is the joint process estimator 60 describedabove.

Illustrating the operation of the ratiometric method of the presentinvention, FIGS. 14, 15 and 16 show signals measured for use indetermining the saturation of oxygenated arterial blood using areference processor of the present invention which employs theratiometric method, i.e., the signals S.sub.λa (t)=S.sub.λred1 (t),S.sub.λb (t)=S.sub.λred2 (t), and S.sub.λc (t)=S.sub.λIR (t). A firstsegment 14a, 15a, and 16a of each of the signals is relativelyundisturbed by motion artifact, i.e., the patient did not movesubstantially during the time period in which these segments weremeasured. These segments 14a, 15a, and 16a are thus generallyrepresentative of the desired plethysmographic waveform at each of themeasured wavelengths. A second segment 14b, 15b, and 16b of each of thesignals is affected by motion artifact, i.e., the patient did moveduring the time period in which these segments were measured. Each ofthese segments 14b, 15b, and 16b shows large motion induced excursionsin the measured signal. A third segment 14c, 15c, and 16c of each of thesignals is again relatively unaffected by motion artifact and is thusgenerally representative of the desired plethysmographic waveform ateach of the measured wavelengths.

FIG. 17 shows the noise reference signal n'(t)=n.sub.λa -ω_(r4) n.sub.λb(t), as determined by a reference processor of the present inventionutilizing the ratiometric method. As discussed previously, the noisereference signal n'(t) is correlated to the undesired signal portionsn.sub.λa, n.sub.λb, and n.sub.λc. Thus, a first segment 17a of the noisereference signal n'(t) is generally flat, corresponding to the fact thatthere is very little motion induced noise in the first segments 14a,15a, and 16a of each signal. A second segment 17b of the noise referencesignal n'(t) exhibits large excursions, corresponding to the largemotion induced excursions in each of the measured signals. A thirdsegment 17c of the noise reference signal n'(t) is generally flat, againcorresponding to the lack of motion artifact in the third segments 14a,14b, and 14c of each measured signal.

FIGS. 18 and 19 show the approximations Y'.sub.λa (t) and Y'.sub.λc (t)to the desired signals Y.sub.λa (t) and Y.sub.λc (t) as estimated by thejoint process estimator 60 using a noise reference signal n'(t)determined by the ratiometric method. Note that the scale of FIGS. 14through 19 is not the same for each figure to better illustrate changesin each signal. FIGS. 18 and 19 illustrate the effect of the jointprocess estimator adaptive noise canceler using the noise referencesignal n'(t) as determined by the reference processor of the presentinvention using the ratiometric method. Segments 18b and 19b are notdominated by motion induced noise as were segments 14b, 15b, and 16b ofthe measured signals. Additionally, segments 18a, 19a, 18c, and 19c havenot been substantially changed from the measured signal segments 14a,15a, 16a, 14c, 15c, and 16c where there was no motion induced noise.

Illustrating the operation of the constant saturation method of thepresent invention, FIGS. 20 and 21 show signals measured for input to areference processor of the present invention which employs the constantsaturation method, i.e., the signals S.sub.λa (t)=S.sub.λred (t) andS.sub.λb (t)=S_(AIR) (t). A first segment 20a and 21a of each of thesignals is relatively undisturbed by motion artifact, i.e., the patientdid not move substantially during the time period in which thesesegments were measured. These segments 20a and 21a are thus generallyrepresentative of the desired plethysmographic waveform at each of themeasured wavelengths. A second segment 20b and 21b of each of thesignals is affected by motion artifact, i.e., the patient did moveduring the time period in which these segments were measured. Each ofthese segments 20b and 21b shows large motion induced excursions in themeasured signal. A third segment 20c and 21c of each of the signals isagain relatively unaffected by motion artifact and is thus generallyrepresentative of the desired plethysmographic waveform at each of themeasured wavelengths.

FIG. 22 shows the noise reference signal n'(t)=n.sub.λa (t)-ω_(s4)n.sub.λb (t), as determined by a reference processor of the presentinvention utilizing the constant saturation method. Again, the noisereference signal n'(t) is correlated to the undesired signal portionsn.sub.λa and n.sub.λb. Thus, a first segment 22a of the noise referencesignal n'(t) is generally flat, corresponding to the fact that there isvery little motion induced noise in the first segments 20a and 21a ofeach signal. A second segment 22b of the noise reference signal n'(t)exhibits large excursions, corresponding to the large motion inducedexcursions in each of the measured signals. A third segment 22c of thenoise reference signal n'(t) is generally flat, again corresponding tothe lack of motion artifact in the third segments 20b and 21c of eachmeasured signal.

FIGS. 23 and 24 show the approximations Y'.sub.λa (t) and Y'.sub.λb (t)to the desired signals Y.sub.λa (t) and Y.sub.λb (t) as estimated by thejoint process estimator 60 using a noise reference signal n'(t)determined by the constant saturation method. Note that the scale ofFIGS. 20 through 24 is not the same for each figure to better illustratechanges in each signal. FIGS. 23 and 24 illustrate the effect of thejoint process estimator adaptive noise canceler using the noisereference signal n'(t) as determined by a reference processor of thepresent invention utilizing the constant saturation method. Segments 23band 24b are not dominated by motion induced noise as were segments 20band 21b of the measured signals. Additionally, segments 23a, 24a, 23c,and 24c have not been substantially changed from the measured signalsegments 20a, 21a, 20c, and 21c where there was no motion induced noise.

METHOD FOR ESTIMATING DESIRED PORTIONS OF MEASURED SIGNALS IN A PULSEOXIMETER

A copy of a computer program subroutine, written in the C programminglanguage, calculates a noise reference signal n'(t) using theratiometric method and, using a joint process estimator 60, estimatesthe desired signal portions of two measured signals, each having anundesired portion which is correlated to the noise reference signaln'(t) and one of which was not used to calculate the noise referencesignal n'(t), is appended in Appendix A. For example, S.sub.λa(t)=S.sub.λred1 (t)=S.sub.λ650nm (t) and S.sub.λc (t)=S.sub.λIR(t)=S.sub.λ940nm (t) can be input to the computer subroutine. On skilledin the art will realize that S.sub.λa (t)=S.sub.λ_(red2)(t)=S.sub.λ685nm (t) and S.sub.λc (t)=S.sub.λIR (t)=S.sub.λ940nm (t)will also work. This subroutine is one way to implement the stepsillustrated in the flowchart of FIG. 8 for a monitor particularlyadapted for pulse oximetry.

The program estimates the desired signal portions of two light energysignals, one preferably corresponding to light in the visible red rangeand the other preferably corresponding to light in the infrared rangesuch that a determination of the amount of oxygen available to the body,or the saturation of oxygen in the arterial blood, may be made. Thecalculation of the saturation is performed in a separate subroutine.Various methods for calculation of the oxygen saturation are known tothose skilled in the art. One such calculation is described in thearticles by G. A. Mook, et al, and Michael R. Neuman cited above. Oncethe concentration of oxygenated hemoglobin and deoxygenated hemoglobinare determined, the value of the saturation is determined similarly toequations (73) through (80) wherein measurements at times t₁ and t₂ aremade at different, yet proximate times over which the saturation isrelatively constant. For pulse oximetry, the average saturation at timet=(t₁ +t₂)/2 is then determined by: ##EQU11##

Using the ratiometric method, three signals S.sub.λa (t), S.sub.λb (t),and S.sub.λc (t) are input to the subroutine. S.sub.λa (t) and S.sub.λb(t) are used to calculate the noise reference signal n'(t). As describedabove, the wavelengths of light at which S.sub.λa (t) and S.sub.λb (t)are measured are chosen to satisfy the relationship of equation (84).Once the noise reference signal n'(t) is determined, the desired signalportions Y.sub.λa (t) and Y.sub.λc (t) of the measured signals S.sub.λa(t) and S.sub.λc (t) are estimated for use in calculation of the oxygensaturation.

The correspondence of the program variables to the variables defined inthe discussion of the joint process estimator is as follows:

Δ_(m) (t)=nc !.Delta

Γ_(f),m(t) =nc !.fref

Γ_(b),m (t)=nc !.bref

f_(m) (t)=nc !.ferr

b_(m) (t)=nc !.berr

ℑ_(m) (t)=nc !.Fswsqr

β_(m) (t)=nc !.Bswsqr

γ_(m) (t)=nc !.Gamma

ρ_(m),λa (t)=nc !.Roh₋₋ a

ρ_(m)λc (t)=nc !.Roh₋₋ c

e_(m),λa (t)=nc !.err₋₋ a

e_(m),λc (t)=nc !.err₋₋ c

e_(m),λc (t)=nc !.K₋₋ a

κ_(m),λa (t)=nc !.K₋₋ c

A first portion of the program performs the initialization of theregisters 90, 92, 96, and 98 and intermediate variable values as in the"INITIALIZE NOISE CANCELER" box 120 and equations (40) through (44) andequations (61), (62), (65), and (66). A second portion of the programperforms the time updates of the delay element variables 110 where thevalue at the input of each delay element variable 110 is stored in thedelay element variable 110 as in the "TIME UPDATE OF Z⁻¹ ! ELEMENTS" box130.

A third portion of the program calculates the noise reference signal, asin the "CALCULATE NOISE REFERENCE (n'(t)) FOR TWO MEASURED SIGNALSAMPLES" box 140 using the proportionality constant ω_(r4) determined bythe ratiometric method as in equation (85).

A fourth portion of the program performs the zero-stage update as in the"ZERO-STAGE UPDATE" box 150 where the zero-stage forward predictionerror f_(o) (t) and the zero-stage backward prediction error b_(o) (t)are set equal to the value of the noise reference signal n'(t) justcalculated. Additionally, zero-stage values of intermediate variables ℑ₀(t) and β₀ (t) (nc !.Fswsqr and nc !.Bswsqr in the program) arecalculated for use in setting register 90, 92, 96, and 98 values in theleast-squares lattice predictor 70 and the regression filters 80a and80b.

A fifth portion of the program is an iterative loop wherein the loopcounter, m, is reset to zero with a maximum of m=NC₋₋ CELLS, as in the"m=0" box 160 in FIG. 8. NC₋₋ CELLS is a predetermined maximum value ofiterations for the loop. A typical value of NC₋₋ CELLS is between 60 and80, for example. The conditions of the loop are set such that the loopiterates a minimum of five times and continues to iterate until a testfor conversion is met or m=NC₋₋ CELLS. The test for conversion iswhether or not the sum of the weighted sum of forward prediction errorsplus the weighted sum of backward prediction errors is less than a smallnumber, typically 0.00001 (i.e, ℑ_(m) (t)+β_(m) (t)≦0.00001).

A sixth portion of the program calculates the forward and backwardreflection coefficient Γ_(m),f (t) and Γ_(m),b (t) register 90 and 92values (nc !.fref and nc !.bref in the program) as in the "ORDER UPDATEm^(th) -STAGE OF LSL-PREDICTOR" box 170 and equations (49) and (50).Then forward and backward prediction errors f_(m) (t) and b_(m) (t) (nc!.ferr and nc !.berr in the program) are calculated as in equations (51)and (52). Additionally, intermediate variables ℑ_(m) (t), β_(m) (t) andγ_(m) (t) (nc !.Fswsqr, nc !.Bswsqr, nc !.Gamma in the program) arecalculated, as in equations (53), (54), and (55). The first cycle of theloop uses the values for nc 0!.Fswsqr and nc 0!.Bswsqr calculated in theZERO-STAGE UPDATE portion of the program.

A seventh portion of the program, still within the loop, calculates theregression coefficient κ_(m),λa (t) and κ_(m),λc (t) register 96 and 98values (nc !.K₋₋ a and nc !.K₋₋ c in the program) in both regressionfilters, as in the "ORDER UPDATE m^(th) STAGE OF REGRESSION FILTER(S)"box 180 and equations (57) through (68). Intermediate error signals andvariables e_(m),λa (t), e_(m),λc (t), ρ_(m),λa (t), and ρ_(m),λc (t) (nc!.err₋₋ a and nc !.err₋₋ c, nc !.roh₋₋ a, and nc !.roh₋₋ c in thesubroutine) are also calculated as in equations (58), (64), (56), and(60), respectively.

The test for convergence of the joint process estimator is performedeach time the loop iterates, analogously to the "DONE" box 190. If thesum of the weighted sums of the forward and backward prediction errorsℑ_(m) (t)+β_(m) (t) is less than or equal to 0.00001, the loopterminates. Otherwise, the sixth and seventh portions of the programrepeat.

When either the convergence test is passed or m=NC₋₋ CELLS, an eighthportion of the program calculates the output of the joint processestimator 60 adaptive noise canceler as in the "CALCULATE OUTPUT" box200. This output is a good approximation to both of the desired signalsY'.sub.λa (t) and Y'.sub.λc (t) for the set of samples S.sub.λa (t),S.sub.λb (t), and S.sub.λc (t) input to the program. After many sets ofsamples are processed by the joint process estimator, a compilation ofthe outputs provides output waves which are good approximations to theplethysmographic wave at each wavelength, λa and λc.

Another copy of a computer program subroutine, written in the Cprogramming language, which calculates a noise reference signal n'(t)using the constant saturation method and, using a joint processestimator 60, estimates a good approximation to the desired signalportions of two measured signals, each having an undesired portion whichis correlated to the noise reference signal n'(t) and each having beenused to calculate the noise reference signal n'(t), is appended inAppendix B. This subroutine is another way to implement the stepsillustrated in the flowchart of FIG. 8 for a monitor particularlyadapted for pulse oximetry. The two signals are measured at twodifferent wavelengths λa and λb, where λa is typically in the visibleregion and λb is typically in the infrared region. For example, in oneembodiment of the present invention, tailored specifically to performpulse oximetry using the constant saturation method, λa=660 nm andλb=940 nm.

The correspondence of the program variables to the variables defined inthe discussion of the joint process estimator is as follows:

Δ_(m) (t)=nc !.Delta

Γ_(f),m (t)=nc !.fref

Γ_(b),m (t)=nc !.bref

f_(m) (t)=nc !.ferr

b_(m) (t)=nc !.berr

ℑ_(m) (t)=nc !.Fswsqr

β_(m) (t)=nc !.Bswsqr

γ(t)=nc !.Gamma

ρ_(m),λa (t)=nc !.Roh₋₋ a

ρ_(m)λb (t)=nc !.Roh₋₋ b

e_(m),λa (t)=nc !.err₋₋ a

e_(m),λb (t)=nc !.err₋₋ b

κ_(m),λa (t)=nc !.K₋₋ a

κ_(m),λb (t)=nc !.K₋₋ b

First and second portions of the subroutine are the same as the firstand second portions of the above described subroutine tailored for theratiometric method of determining the noise reference signal n'(t).

A third portion of the subroutine calculates the noise reference signal,as in the "CALCULATE NOISE REFERENCE (n'(t)) FOR TWO MEASURED SIGNALSAMPLES" box 140 for the signals S.sub.λa (t) and S.sub.λb (t) using thea proportionality constant ω_(s4) (t) determined by the constantsaturation method as in equations (90) and (91). The saturation iscalculated in a separate subroutine and a value of ω_(s4) (t) isimported to the present subroutine for estimating the desired portionsY.sub.λa (t) and Y.sub.λb (t) of the composite measured signals S.sub.λa(t) and S.sub.λb (t).

Fourth, fifth, and sixth portions of the subroutine are similar to thefourth, fifth, and sixth portions of the above described programtailored for the ratiometric method. However, the signals being used toestimate the desired signal portions Y.sub.λa (t) and Y.sub.λb (t) inthe present subroutine tailored for the constant saturation method, areS.sub.λa (t) and S.sub.λb (t), the same signals that were used tocalculate the noise reference signal n'(t).

A seventh portion of the program, still within the loop begun in thefifth portion of the program, calculates the regression coefficientregister 96 and 98 values κ_(m),λa (t) and κ_(m),λb (t) (nc !.K₋₋ a andnc !.K₋₋ b in the program) in both regression filters, as in the "ORDERUPDATE m^(th) STAGE OF REGRESSION FILTER(S)" box 180 and equations (57)through (67). Intermediate error signals and variables e_(m),λa (t),e_(m),λb (t), ρ_(m),λa (t), and ρ_(m),λb (t) (nc !.err₋₋ a and nc!.err₋₋ b, nc !.roh₋₋ a, and nc !.roh₋₋ b in the subroutine) are alsocalculated as in equations (58), (63), (56), and (59), respectively.

The loop iterates until the test for convergence is passed, the testbeing the same as described above for the subroutine tailored for theratiometric method. The output of the present subroutine is a goodapproximation to the desired signals Y'.sub.λa (t) and Y'.sub.λb (t) forthe set of samples S.sub.λa (t) and S.sub.λb (t) input to the program.After approximations to the desired signal portions of many sets ofmeasured signal samples are estimated by the joint process estimator, acompilation of the outputs provides waves which are good approximationsto the plethysmographic wave at each wavelength, λa and λb. Theestimating process of the iterative loop is the same in eithersubroutine, only the sample values S.sub.λa (t) and S.sub.λc (t) orS.sub.λa (t) and S.sub.λb (t) input to the subroutine for use inestimation of the desired signal portions Y.sub.λa (t) and Y.sub.λc (t)or Y.sub.λa (t) and Y.sub.λb (t) and how the noise reference signaln'(t) is calculated are different for the ratiometric method and theconstant saturation methods.

Independent of the method used, ratiometric or constant saturation, theapproximations to the desired signal values Y'.sub.λa (t) and Y'.sub.λc(t) or Y'.sub.λa (t) and Y'.sub.λb (t) are input to a separatesubroutine in which the saturation of oxygen in the arterial blood iscalculated. If the constant saturation method is used, the saturationcalculation subroutine also determines a value for the proportionalityconstant ω_(s4) (t) as defined in equations (90) and (91) and discussedabove. The concentration of oxygenated arterial blood can be found fromthe approximations to the desired signal values since the desiredsignals are made up of terms comprising x(t), the thickness of arterialblood in the finger; absorption coefficients of oxygenated andde-oxygenated hemoglobin, at each measured wavelength; and C_(HbO2) (t)and C_(Hb) (t), the concentrations of oxygenated and de-oxygenatedhemoglobin, respectively. The saturation is a ratio of the concentrationof one constituent, As, with respect to the total concentration ofconstituents in the volume containing A₅ and A₆. Thus, the thickness,x(t), is divided out of the saturation calculation and need not bepredetermined. Additionally, the absorption coefficients are constant ateach wavelength. The saturation of oxygenated arterial blood is thendetermined as in equations (95) and (96).

While one embodiment of a physiological monitor incorporating aprocessor of the present invention for determining a noise referencesignal for use in an adaptive noise canceler to remove erratic noisecomponents from a physiological measurement has been described in theform of a pulse oximeter, it will be obvious to one skilled in the artthat other types of physiological monitors may also employ the abovedescribed techniques for noise reduction on a composite measured signalin the presence of noise.

Furthermore, it will be understood that transformations of measuredsignals other than logarithmic conversion and determination of aproportionality factor which allows removal of the desired signalportions for determination of a noise reference signal are possible.Additionally, although the proportionality factor ω has been describedherein as a ratio of a portion of a first signal to a portion of asecond signal, a similar proportionality constant determined as a ratioof a portion of a second signal to a portion of a first signal couldequally well be utilized in the processor of the present invention. Inthe latter case, a noise reference signal would generally resemblen'(t)=n.sub.λb (t)-ωn.sub.λa (t).

It will also be obvious to one skilled in the art that for mostphysiological measurements, two wavelengths may be determined which willenable a signal to be measured which is indicative of a quantity of acomponent about which information is desired. Information about aconstituent of any energy absorbing physiological material may bedetermined by a physiological monitor incorporating a signal processorof the present invention and an adaptive noise canceler by determiningwavelengths which are absorbed primarily by the constituent of interest.For most physiological measurements, this is a simple determination.

Moreover, one skilled in the art will realize that any portion of apatient or a material derived from a patient may be used to takemeasurements for a physiological monitor incorporating a processor ofthe present invention and an adaptive noise canceler. Such areas includea digit such as a finger, but are not limited to a finger.

One skilled in the art will realize that many different types ofphysiological monitors may employ a signal processor of the presentinvention in conjunction with an adaptive noise canceler. Other types ofphysiological monitors include, but are in not limited to, electroncardiographs, blood pressure monitors, blood gas saturation (other thanoxygen saturation) monitors, capnographs, heart rate monitors,respiration monitors, or depth of anesthesia monitors. Additionally,monitors which measure the pressure and quantity of a substance withinthe body such as a breathalizer, a drug monitor, a cholesterol monitor,a glucose monitor, a carbon dioxide monitor, a glucose monitor, or acarbon monoxide monitor may also employ the above described techniquesfor removal of undesired signal portions.

Furthermore, one skilled in the art will realize that the abovedescribed techniques of noise removal from a composite signal includingnoise components can also be performed on signals made up of reflectedenergy, rather than transmitted energy. One skilled in the art will alsorealize that a desired portion of a measured signal of any type ofenergy, including but not limited to sound energy, X-ray energy, gammaray energy, or light energy can be estimated by the noise removaltechniques described above. Thus, one skilled in the art will realizethat the processor of the present invention and an adaptive noisecanceler can be applied in such monitors as those using ultrasound wherea signal is transmitted through a portion of the body and reflected backfrom within the body back through this portion of the body.Additionally, monitors such as echo cardiographs may also utilize thetechniques of the present invention since they too rely on transmissionand reflection.

While the present invention has been described in terms of aphysiological monitor, one skilled in the art will realize that thesignal processing techniques of the present invention can be applied inmany areas, including but not limited to the processing of aphysiological signal. The present invention may be applied in anysituation where a signal processor comprising a detector receives afirst signal which includes a first desired signal portion and a firstundesired signal portion and a second signal which includes a seconddesired signal portion and a second undesired signal portion. The firstand second signals propagate through a common medium and the first andsecond desired signal portions are correlated with one another.Additionally, at least a portion of the first and second undesiredsignal portions are correlated with one another due to a perturbation ofthe medium while the first and second signals are propagating throughthe medium. The processor receives the first and second signals andcombines the first and second signals to generate a noise referencesignal in which the primary component is derived from the first andsecond undesired signal portions. Thus, the signal processor of thepresent invention is readily applicable to numerous signal processingareas. ##SPC1##

What is claimed:
 1. A method of determining a noise reference signalfrom first and second physiological measurement signals, the methodcomprising the steps of:generating the first and second physiologicalmeasurement signals, the first measurement signal comprising a firstdesired signal portion and a first noise portion and the secondmeasurement signal comprising a second desired signal portion and asecond noise portion; selecting a signal coefficient which isproportional to a ratio of predetermined attributes of said firstdesired signal portion and predetermined attributes of said seconddesired signal portion; inputting said first measurement signal and saidsignal coefficient into a signal multiplier wherein said firstmeasurement signal is multiplied by said signal coefficient therebygenerating a first intermediate signal; and inputting said secondmeasurement signal and said first intermediate signal into a signalsubtractor wherein said first intermediate signal is subtracted fromsaid second measurement signal thereby generating a reference signalhaving a primary component which is derived from said first and secondnoise signal portions.
 2. The method of claim 1 wherein said first andsecond measurement signals collectively represent a blood-gas saturationof a patient.
 3. A method of removing a motion artifact signal from asignal derived from a physiological measurement comprising the stepsof:passing at least first and second wavelengths of electromagneticradiation through living tissue; detecting the first and secondwavelengths with a detector following propagation through the livingtissue to generate, respectively, a first signal having a physiologicalmeasurement component and a motion artifact component and a secondsignal having a physiological measurement component and a motionartifact component; and deriving from said first and second signals amotion artifact reference signal which is a primary function of saidfirst and second signals' motion artifact components.
 4. A method asdefined in claim 3 further comprising the step of inputting said motionartifact reference signal into an adaptive noise canceler to produce anoutput signal which is a primary function of said first signalphysiological measurement component.
 5. The method of claim 1 whereinthe step of generating first and second physiological measurementscomprises passing first and second wavelengths of light energy through alight-absorptive physiologic medium and detecting said light energy. 6.The method of claim 5 wherein the absorptive medium comprises livingtissue of a human digit.
 7. The method of claim 5 wherein the firstmeasurement signal is derived from light energy of the first wavelength,and the second measurement signal is derived from light energy of thesecond wavelength, and the method further comprises selecting the firstand second wavelengths of light such that the first and second desiredsignal portions are generally linearly dependent.
 8. The method of claim5, further comprising selecting the first wavelength (λ_(A)) and thesecond wavelength (λ_(B)) such that the equation ε_(hemoglobin),λA/ε_(oxyhemoglobin),λA =ε_(hemoglobin),λB /ε_(oxyhemoglobin),λB issubstantially satisfied.
 9. The method of claim 1 wherein the first andsecond physiological measurements are taken in close proximity.
 10. Themethod of claim 1 wherein the step of selecting a signal coefficientcomprises selecting a coefficient which results in a substantialcancellation of the desired signal portions from the second measurementsignal and the intermediate signal in said step of inputting.
 11. Themethod of claim 1 further comprising the step of applying the noisereference signal to at least one of the first and second measurementsignals to substantially remove a respective noise portion therefrom.12. The method of claim 1 wherein each of the first and second desiredsignal portions contain information necessary for determining a desiredphysiological parameter.
 13. The method of claim 12 further comprisingthe steps of:applying the noise reference signal to the first and secondmeasurement signals to extract the first and second desired signalportions; and combining the first and second desired signal portionsextracted in said step of applying to generate a signal indicative ofthe physiological parameter.
 14. The method of claim 3 wherein the firstand second wavelengths are selected such that the respectivephysiological measurement components of the first and second signals aregenerally linearly dependent.
 15. The method of claim 14 wherein thefirst wavelength (λ_(A)) and the second wavelength (λ_(B)) are selectedsuch that the equation ε_(hemoglobin),λA /ε_(oxyhemoglobin),λA=ε_(hemoglobin),λB /ε_(oxyhemoglobin),λB is substantially satisfied. 16.The method of claim 3 wherein the step of deriving comprises multiplyingat least one of the first and second signals by a proportionalitycoefficient, the proportionality coefficient selected such that therespective physiological measurement components of the first and secondsignals are substantially equal following said step of multiplying. 17.The method of claim 16 wherein the proportionality coefficient is aconstant that is based on selected absorption coefficients of the firstand second wavelengths.
 18. The method of claim 3 wherein the step ofpassing comprises propagating light energy through a human digit. 19.The method of claim 3 wherein the physiological measurement componentsof the first and second signals contain information which, incombination, is indicative of a blood-gas saturation of a patient. 20.The method of claim 19 wherein the physiological measurement componentsof the first and second signals contain information which, incombination, is indicative of a saturation level of oxygen withinarterial blood of the patient.
 21. A method of taking a physiologicmeasurement, the method comprising the steps of:passing light energy ofat least first and second wavelengths through a light-absorptivephysiologic medium to a light-sensitive detector to generate,respectively, first and second signals, each of the first and secondsignals comprising a physiologic measurement component and an artifactcomponent, the first and second wavelengths selected based on lightabsorption characteristics of the physiologic medium such that agenerally linear relationship exists between the physiologic measurementcomponents of the first and second signals; and combining the first andsecond signals to generate a noise reference signal which is primarilycorrelated to the artifact components of the first and second signals,the step of combining comprising utilizing the generally linearrelationship to substantially cancel the physiologic measurementcomponents.
 22. The method of claim 21 wherein the step of combiningcomprises multiplying the first signal by a proportionality constant togenerate an intermediate signal and then combining the intermediatesignal with the second signal, the proportionality constant selectedsuch that the physiologic measurement components substantially cancel insaid step of combining.
 23. The method of claim 21 wherein the firstwavelength (λ_(A)) and the second wavelength (λ_(B)) are selected suchthat the equation ε_(hemoglobin),λA /ε_(oxyhemoglobin),λA=ε_(hemoglobin),λB /ε_(oxyhemoglobin),λB is substantially satisfied. 24.The method of claim 21 wherein the step of passing light energy througha light-absorptive physiologic medium comprises passing the light energythrough at least a portion of a human digit.
 25. The method of claim 21wherein the physiological measurement components of the first and secondsignals collectively indicate a blood-gas saturation of a patient. 26.The method of claim 25 wherein the physiological measurement componentsrepresent a saturation level of oxygen within blood of the patient. 27.The method of claim 21, further comprising the step of applying thenoise reference signal to at least one of the first and the secondsignals to generate a physiologic measurement signal in which theartifact components are substantially absent.
 28. A method of taking ataking a physiological measurement, the method comprising the stepsof:passing light energy of at least first and second wavelengths througha light absorptive physiologic medium to a light-sensitive detector togenerate, respectively, first and second signals, each of the first andsecond signals comprising a desired physiologic measurement componentand an artifact component, the first and second wavelengths selectedbased on light absorption characteristics of the physiologic medium suchthat a substantially linear relationship exists between the desirephysiologic measurement components of the first and second signals;extracting the desired physiological measurement component from saidfirst or said second signals by multiplying at least one of the firstand second signals by a proportionality constant and subtracting said atleast one of the first and second signals multiplied by saidproportionality constant from the other one of said first and secondsignals; and determining the physiological measurement.
 29. A method oftaking a physiological measurement, the method comprising the stepsof:passing light energy of at least first and second wavelengths througha light-absorptive physiologic medium to a light-sensitive detector togenerate, respectively, first and second signals, each of the first andsecond signals comprising a physiologic measurement portion and asecondary portion, the first and second wavelengths selected based onlight absorption characteristics of the physiologic medium such that asubstantially linear relationship exists between the secondary portionof the first and second signals; and combining said first and secondsignals to generate a signal which is primarily correlated to thephysiologic measurement portions, the step of combining comprising usingthe linear relationship to substantially remove the secondary portion ofthe signals.
 30. The method of claim 29, wherein said secondary portionof said signals contains information indicative of venous bloodabsorption and said physiologic measurement portion contains informationindicative of arterial blood absorption.
 31. The method of claim 29,wherein said step of combining comprises multiplying one of said firstand second signals by a constant and subtracting the one of said firstand second signals from the other one of said first and second signals.32. The method of claim 29, wherein said step of combining comprises thesteps of:multiplying one of said first or second signal by each of aplurality of possible constants and subtracting each of said multipliedvalues from the other one of said first and second signals to obtain aplurality of possible signals intermediate signals; and evaluating eachof said plurality of possible signals to calculate one or more that mostclosely correlate to the physiologic measurement component of saidsignals.
 33. The method of claim 32, wherein said step of evaluatingcomprises calculating a power value for each of said plurality ofpossible signals and selecting the signal with the minimum power value.34. The method of claim 32, wherein said step of evaluating comprisescalculating a power value for each of said plurality of possible signalsand selecting the signal with the maximum power value.